Safety Scale:    69.18

Safety Index: 56.17

(adsbygoogle = window.adsbygoogle || []).push({}); Crime > Australia > Sydney Crime in Sydney, Australia Compare Sydney with: jQuery(document).ready(function($) { jQuery('#city_selector_city_id1').autocomplete({ source: 'https://www.numbeo.com/common/CitySearchJson', minLength: 1, delay: 3, autoFocus: true, select: function(event, ui) { event.preventDefault(); $('#city_selector_city_id1').val(ui.item.label); $('#city_id1').val(ui.item.value); jQuery('#dispatch_form').submit(); }, focus: function(event, ui) { event.preventDefault(); } }) .keydown(function(e){ if (e.keyCode === 13){ if ($('#city_id1').val() == '') { e.preventDefault(); } var val = $('#city_selector_city_id1').val(); $('#name_city_id1').val(val); } }); // end autocompleter definition }); // end document ready Do you live in Sydney? Add data for Sydney Index Crime Index: 37.89 Safety Index: 62.11

Safety Index: 55.89

afety Scale:    66.54    55.89

Water Pollution Low 27.59 Low 32.97

Water Pollution Low 27.59

Very High 82.10 Very High 87.07

Quality of Green and Parks Very High 82.10

CO2 Emission Index:    5,446.92

CO2 Emission Index:

Time Index (in minutes):    35.88

Time Index (in minutes):

CO2 Emission Index:    6,489.83

CO2 Emission Index:    6,489.83    5,764.28

Time Index (in minutes):    43.18

Time Index (in minutes):    43.18    42.14

CO2 Emission Index:    6,564.11  

Time Index (in minutes):    38.87

CO2 Emission Index:    6,564.11    4,587.53

Time Index (in minutes):    38.87    33.06

CO2 Emission Index:    5,419.09

CO2 Emission Index:    5,419.09    5,711.01

Time Index (in minutes):    43.98

Time Index (in minutes):    43.98    39.99

   3,956.71

CO2 Emission Index:    3,956.71    3,308.53

Time Index (in minutes):

Time Index (in minutes):    32.07    41.33

   122.73    142.91 Time Index (in minutes):    34.00

12  Canada 0.890

12  Canada 0.890 16.

12  Canada 0.890

12  Canada 0.890

35  Italy 0.814

35  Italy 0.814

35  Italy 0.814

1  Australia 0.939

1  Australia 0.939

1  Australia

1  Australia 0.939

35  Italy 0.814 16.3 10.9 26

Google also says location records stored in My Activity are used to target ads. Ad buyers can target ads to specific locations — say, a mile radius around a particular landmark — and typically have to pay more to reach this narrower audience. While disabling “Web & App Activity” will stop Google from storing location markers, it also prevents Google from storing information generated by searches and other activity. That can limit the effectiveness of the Google Assistant, the company’s digital concierge. Sean O’Brien, a Yale Privacy Lab researcher with whom the AP shared its findings, said it is “disingenuous” for Google to continuously record these locations even when users disable Location History. “To me, it’s something people should know,” he said.

Google says that will prevent the company from remembering where you’ve been. Google’s support page on the subject states: “You can turn off Location History at any time. With Location History off, the places you go are no longer stored.” That isn’t true. Even with Location History paused, some Google apps automatically store time-stamped location data without asking. (It’s possible, although laborious, to delete it .)

Data mining can be defined broadly as: “the application of specific algorithms for extracting patterns from data.” [17]

hus it becomes possible to see how ques-tions around data use need to shift from asking what is in the data, to include discussions of how the data is structured, and how this structure codifies value systems and social practices, subject positions and forms of visibility and invisi-bility (and thus forms of surveillance), along with the very ideas of crisis, risk governance and preparedness. Practices around big data produce and perpetuate specific forms of social engagement as well as understandings of the areas affected and the people being served.

Big data is not just about knowing more. It could be – and should be – about knowing better or about changing what knowing means. It is an ethico- episteme-ontological- political matter. The ‘needle in the haystack’ metaphor conceals the fact that there is no such thing as one reality that can be revealed. But multiple, lived are made through mediations and human and technological assemblages. Refugees’ realities of intersecting intelligences are shaped by the ethico- episteme-ontological politics of big data.

Burns (2015) builds on this to investigate how within digital humanitarianism discourses, big data produce and perform subjects ‘in need’ (individuals or com-munities affected by crises) and a humanitarian ‘saviour’ community that, in turn, seeks answers through big data

Crises are often not a crisis of information. It is often not a lack of data or capacity to analyse it that prevents ‘us’ from pre-venting disasters or responding effectively. Risk management fails because there is a lack of a relational sense of responsibility. But this does not have to be the case. Technologies that are designed to support collaboration, such as what Jasanoff (2007) terms ‘technologies of humility’, can be better explored to find ways of framing data and correlations that elicit a greater sense of relational responsibility and commitment.

In some ways this constitutes the production of ‘liquid resilience’ – a deflection of risk to the individuals and communities affected which moves us from the idea of an all-powerful and knowing state to that of a ‘plethora of partial projects and initiatives that are seeking to harness ICTs in the service of better knowing and governing individuals and populations’ (Ruppert 2012: 118)

Indeed, as Chandler (2015: 9) also argues, crowdsourcing of big data does not equate to a democratisation of risk assessment or risk governance:

However, with these big data collections, the focus becomes not the individu-al’s behaviour but social and economic insecurities, vulnerabilities and resilience in relation to the movement of such people. The shift acknowledges that what is surveilled is more complex than an individual person’s movements, communica-tions and actions over time.

Andrejevic and Gates (2014: 190) suggest that ‘the target becomes the hidden patterns in the data, rather than particular individuals or events’. National and local authorities are not seeking to monitor individuals and discipline their behaviour but to see how many people will reach the country and when, so that they can accommodate them, secure borders, and identify long- term social out-looks such as education, civil services, and impacts upon the host community (Pham et al. 2015).

as boyd and Crawford argue, ‘without taking into account the sample of a data set, the size of the data set is meaningless’ (2012: 669). Furthermore, many tech-niques used by the state and corporations in big data analysis are based on probabilistic prediction which, some experts argue, is alien to, and even incom-prehensible for, human reasoning (Heaven 2013). As Mayer-Schönberger stresses, we should be ‘less worried about privacy and more worried about the abuse of probabilistic prediction’ as these processes confront us with ‘profound ethical dilemmas’ (in Heaven 2013: 35).

There is also a need for mechanisms to support transformations and processesover time, both for scientific data and scientific ideas. These mechanisms should not only help the user visualize but also express time and change.

Contrary to paper notes, computer files do not display the traces or versions that led to their final state.

This is related to the fact that biology researchers are in a creative process and reflect on their decisions in order to explore new leads or justify their decisions. Paper laboratory notebooks show this temporality ofthoughts.

Temporal Data and Data Temporality: Time is change, not only ord

City Data Exchange is a public-private partnership that explores the possibilities of data exchange. The project investigates the purchase, sale and sharing of a wide variety of data types between different types of users in the city - citizens, public institutions and private companies. The project is a collaboration between the City of Copenhagen, the Capital Region, CLEAN and Hitachi. The idea behind the cooperation is to create a data hub that supports innovation, and which improves the quality of life in the Copenhagen area. The project aims at establishing a marketplace for data owned by both public authorities and private companies. In this way, the project aims to enable large, small and medium-sized enterprises, start-up companies, universities and the public sector to collaborate by consolidating several sources of information. So far, the project has conducted several experiments aimed at organizational and technical setup of a computer market. The technical part of this can be seen on the platform developed by Hitachi, citydataexchange.com .

Open Data DK is a union consisting of a number of Danish municipalities and regions aimed at making public data open and accessible for citizens and businesses. The goal is to increase transparency in public administration and support data-driven growth. The Copenhagen portal for city data contains information about infrastructure, traffic, cultural events and much more. You can find the portal here .

Although the spatio-temporal variation in rumor quantity and content has long been of interest to thefield, collecting data that accounts for temporalandspatial characteristics of rumoring has been extraordinarily difficult to dowith any degree of precision. Some have been able to capture rumoring data with some degree of temporal precision (Bordiaand Rosnow, 1998; Danzig, 1958; Greenberg, 1964) or with some spatial precision (Larsen, 1954), but bridging the two hasbeen difficult. Synthesizing temporal and spatial rumoring data across a wide variety of events had long been beyond thecapabilities of researchers. Simply gathering reliable data on rumoring was already fraught with challenges.

where applicable, any rating in the form of a data trust score that may be assignedto the data fiduciary under section 35;and

the period for which the personal data will beretained in terms of section 10 or where such period is not known, the criteria for determining such period;

Upon receipt of notification, the Authority shall determine whether such breach should be reported by the data fiduciaryto the data principal, taking into account the severity of the harm that may be caused to such data principal or whether some action is required on the part of the data principal to mitigate suchharm.

“Personal data breach”means any unauthorised or accidental disclosure, acquisition, sharing, use, alteration, destruction, loss of access to, of personal data that compromises the confidentiality, integrity or availability of personal data to a data principal;

We noticed that the people who use the data are usually not the same people who produce the data, and they often don’t know where to find the information about the data they try to use. Since the Schematizer already has the knowledge about all the schemas in the Data Pipeline, it becomes an excellent candidate to store information about the data. Meet our knowledge explorer, Watson. The Schematizer requires schema registrars to include documentation along with their schemas. The documentation then is extracted and stored in the Schematizer. To make the schema information and data documentation in the Schematizer accessible to all the teams at Yelp, we created Watson, a webapp that users across the company can use to explore this data. Watson is a visual frontend for the Schematizer and retrieves its information through a set of RESTful APIs exposed by the Schematizer.

Mayor de Blasio and his administration have made progress in meeting their goal of building 200,000 affordable units over the span of a decade, as 21,963 new units were added in 2016, the most in 27 years. However, there continues to be a shortage in East Harlem. Out of the nearly 20,000 affordable units, the city brought to all five boroughs, just 249 units have been built in East Harlem, according to a new report by the Department of Housing and Preservation Development (HPD). To better accommodate these residents, the city plans on expediting the construction of 2,400 units of affordable housing over the next few years, as DNA Info reported.

Furthermore, and differentiating digital time from clock time, he suggests that a lack of adherence to chronological time is compounded by the fact that digital technologies connect with a flow of information that is al-ways and instantly available. He argues that continual change, which is bound up with web services such as social network sites, blogs and the news, is central to the experi-enced need for constant connectivity.

It is not the data that are valuable, he says, but the services powered by them. Some firms are just better at developing new offerings than others.

Kahneman concluded his aforementioned presentation to academics by arguing that computers or robots are better than humans on three essential dimensions: they are better at statistical reasoning and less enamoured with stories; they have higher emotional intelligence; and they exhibit far more wisdom than humans.

But, while Kahneman calls for large-scale replications of priming studies, the argument here is not that we need more studies or data to verify that people indeed miss blatantly obvious gorillas. Instead, we need better interpretation and better theories.

webinar

With over 65% contribution to the total revenue, talent solutions are the most important services and tools included in the LinkedIn business model. Talent solutions include premium recruiting tools for the companies and recruiters to help them find the most suitable employees/partners for their business.

LinkedIn apart from being the best recruitment platform is also a sought-after social networking website by marketers to execute their marketing campaigns. This service contributes to over 18% of the total revenue of the company and offers features which let companies to not only create a company page but also enhance their marketing efforts by creating sponsored content, sponsored InMails and text advertisements.

The mobile version of the website was launched in February 2008 and the company was bought by Microsoft in February 2016 for $26.2 billion.

In one high-profile example, a human swarm challenge by CBS Interactive to predict the Kentucky Derby. The swarm correctly predicted the first four horses, in order, defying 542–1 odds and turning a $20 bet into $10,800.

About 600,000 people visit News Genius a month, Lehman said, a figure that had grown 10 times since before President Donald Trump was inaugurated. And the number of people who annotate a post on Genius each month is now at 10,000, up 30 percent from the start of the year. “More people are using News Genius now than ever,” Lehman said. Meanwhile, overall traffic to the website and apps has grown to 62 million a month.

Soon after, Genius made a definitive push to realize Andreessen’s vision. By 2015, Genius claimed 40 million visitors to its website a month, 1 million of whom had annotated a post.

But the biggest problem with the annotator from Genius’ perspective is that few individuals are using it. After more than two years of development, the Chrome extension has only 12,320 users. It was last updated in June 2016.

Right now, they estimate the global taxi market is worth $108 billion, which is triple the size of the $36-billion ride-hailing market. At the same time, they calculate an average of 15 million ride-hailing trips a day globally, which they expect to increase to 97 million by 2030.

Recent studies have indicated that Uber’s U.S. driver churn has sharply increased this year, to rates as high as 96%. Needless to say, it’s hard (and costly) to maintain double-digit growth rates, when only 4% of mission critical, de facto employees stay on the job for more than a year.

Figure 9. Constraint plots

Only a portion of this table is shown here to demonstrate its form and content. A machine-readable version of the full table is available.

Our posterior samples are available online (10.5281/zenodo.162965).

S7 Data Release 2.0

Acero F., Ackermann M., Ajello M. et al (Fermi-LAT) 2015 arXiv:1501.02003Preprint

The catalog of fakes used to generate the efficiency grids in Section 3 are available in a persistent directory: doi:10.5258/SOTON/D0030.

1- 13 A13 --- Planet Planet 15- 15 I1 --- robust Robust flag (1) 17- 23 F7.3 d Per Orbital period 25- 28 F4.1 Rgeo Rad Planet radius 30- 33 F4.2 Rgeo E_Rad 1{sigma} upper error bound on Rad 35- 38 F4.2 Rgeo e_Rad 1{sigma} lower error bound on Rad 40- 40 A1 --- r_Rad Source of planet-star radius ratio (2) 42- 44 F3.1 solMass Mstar Mass of host star 46- 49 F4.2 solMass E_Mstar 1{sigma} upper error bound on Mstar 51- 54 F4.2 solMass e_Mstar 1{sigma} lower error bound on Mstar 56- 56 I1 --- l_Md Md upper limit flag (3) 58- 63 F6.2 Mgeo Md Planet mass from default prior 65- 69 F5.2 Mgeo E_Md ?="" 1{sigma} upper error bound on Md 71- 74 F4.2 Mgeo e_Md ?="" 1{sigma} lower error bound on Md 76- 81 F6.2 g/cm3 rhod Planet density from default prior 83- 87 F5.2 g/cm3 E_rhod ?="" 1{sigma} upper error bound on rhod 89- 92 F4.2 g/cm3 e_rhod ?="" 1{sigma} lower error bound on rhod 94- 94 I1 --- l_Mh Mh upper limit flag (3) 96-100 F5.1 Mgeo Mh Planet mass from high mass prior 102-107 F6.2 Mgeo E_Mh ?="" 1{sigma} upper error bound on Md 109-112 F4.2 Mgeo e_Mh ?="" 1{sigma} lower error bound on Md 114-119 F6.2 g/cm3 rhoh Planet density from high mass prior 121-126 F6.2 g/cm3 E_rhoh ?="" 1{sigma} upper error bound on rhod 128-131 F4.2 g/cm3 e_rhoh ?="" 1{sigma} lower error bound on rhod 133-155 A23 --- Ref References (4)

WISE J085510.83−071442.5

“In short, they have no history of supporting the machine learning research community and instead they are viewed as part of the disreputable ecosystem of people hoping to hype machine learning to make money.”

Thus, the digital object ossifies out of two histories, one virtual and another visual.Within computation, the object arises out of a desire to create a model of the worldwithin the computer but at the same time out of an attempt to create a whole new visualworld native to the compute

We showhow the rise of large datasets, in conjunction with arising interest in data as scholarly output, contributesto the advent of data sharing platforms in a field trad-itionally organized by infrastructures.

Negative values included when assessing air quality In computing average pollutant concentrations, EPA includes recorded values that are below zero. EPA advised that this is consistent with NEPM AAQ procedures. Logically, however, the lowest possible value for air pollutant concentrations is zero. Either it is present, even if in very small amounts, or it is not. Negative values are an artefact of the measurement and recording process. Leaving negative values in the data introduces a negative bias, which potentially under represents actual concentrations of pollutants. We noted a considerable number of negative values recorded. For example, in 2016, negative values comprised 5.3 per cent of recorded hourly PM2.5 values, and 1.3 per cent of hourly PM10 values. When we excluded negative values from the calculation of one‐day averages, there were five more exceedance days for PM2.5 and one more for PM10 during 2016.

Human cognition loses its personal character. Individuals turn into data, and data become regnant

Traditional approaches to information processing present ‘‘information’’as given, well-defined and stable.

sqlite> .mode column sqlite> .headers on

pandas is a Python module used for manipulation and analysis of tabular data.

pandas official documentation

The alternative, of a regulatory patchwork, would make it harder for the West to amass a shared stock of AI training data to rival China’s.

Data Re-Use. Contractor agrees that any and all Institutional Data exchanged shall be used expressly and solely for the purposes enumerated in the Agreement. UH Institutional Data shall not be distributed, repurposed or shared across other applications, environments, or business units of the Contractor. The Contractor further agrees that no Institutional Data of any kind shall be revealed, transmitted, exchanged or otherwise passed to other vendors or interested parties except on a case-by-case basis as specifically agreed to in writing by a University officer with designated data, security, or signature authority.

For the past 100 years we have been chasing visions of data with a singular passion. Many of the best minds of each new generation have devoted themselves to delivering on the inspired data science promises of their day: intelligence testing, building the computer, cracking the genetic code, creating the internet, and now this. We have in the course of a single century built an entire society, economy and culture that runs on information. Yet we have hardly begun to engineer data ethics appropriate for our extraordinary information carnival. If we do not do so soon, data will drive democracy, and we may well lose our chance to do anything about it.

Introducing Subscribe with Google

Dentro de esta propuesta, bien llamada ‘investigación desde la acción colectiva’ (IAC), “las comunidades hacen parte de la producción del conocimiento como investigadoras y los investigadores e investigadoras hacen parte de las acciones colectivas [de transformación social]”

‘caminar la palabra’, un concepto desarrollado por la minga social y comunitaria de los nasa para señalar la necesidad de hacerse visible, denunciar y tejer conocimientos, resistencias y estrategias de manera colectiva con otros movimientos. Las alianzas requieren la creación de inter-conocimiento y traducción entre movimientos y mundos para permitir la inteligibilidad y una medida de coordinación (

. Cambiar la forma de cambiar para cambiar de manera autónoma y construir una nueva realidad (comunidad, región, nación) desde abajo y a la izquierda, como los zapatistas gustan decir. La autonomía no se logra por medio de la ‘captura del Estado’ sino recuperando del Estado las áreas claves de la vida social que ha colonizado. Crea ámbitos de acción que son autónomos del Estado y nuevos arreglos institucionales con ese propósito (como las conocidas juntas de buen gobierno en los territorios zapatistas). La autonomía pretende el establecimiento de nuevas bases para la vida social.

Maestría en Estudios Interdisciplinarios del Desarrollo de la Universidad del Cauca (un bastión del pensamiento decolonial en América Latina, a pesar de su referencia al desarrollo), busca ser una cátedra abierta en la que pueda tener lugar un diálogo inter-epistémico entre académicos, intelectuales y activistas. Tramas y mingas, al que asisten varios cientos de participantes, en gran parte de movimientos sociales y comunidades de base del suroccidente colombiano, es un maravilloso espacio de conversación entre mundos y conocimientos.

. La ICT tiene conexiones directas con los movimientos de intelectuales y activistas en torno a las nociones de ‘decrecimiento’ y la defensa de los comunes. La ICT, el decrecimiento y los comunes en conjunto constituyen un espacio unificado para el futuro desarrollo de la teoría y la práctica del diseño para la transición. En la siguiente sección voy a proponer unconjunto similar de nociones provenientes de América Latina, incluyendo el post-desarrollo, el buen vivir, los derechos de la naturaleza y las transiciones al post-extractivismo como espacios importantes para profundizar el diseño para la transici

El énfasis en la construcción de lugar y en la práctica colaborativa, así como en el arraigo inequívoco del diseño para la transición a una visión ecológica, constituye, sin duda, una intervención ontológica —una ontología política del diseño

Todo diseño es para ‘uso’ enactivo (pero no involucra sólo ‘usuarios’); produce eficacia operacional (pero no ‘utilidad’); fomenta la autopoiesis de las entidades vivas y de los conjuntos heterogéneos de vida; es consciente de vivir en el pluriverso.

En la creación de ámbitos de conversaciones para la acción pasa, necesariamente, del diseño a la experiencia y viceversa (a través, por ejemplo, de la creación de prototipos y de análisis de escenarios). Se pregunta hasta dónde la creación de nuevos diseños permite la emergencia de mejores dominios de interpretación y de acción (

desde esta perspectiva las organizaciones constituyen conversaciones para la acción. Hay un cierto grado de recurrencia y formalización en estas conversaciones, que Winograd y Flores (1986) caracterizan en términos de actos lingüísticos distintivos. Las organizaciones son redes de compromisos que operan a través de actos lingüísticos, como las promesas y

Las rupturas son momentos en los que se interrumpe el modo habitual de ser-en-el-mundo; cuando ocurre una descomposición de este tipo nuestras prácticas consuetudinarias y el papel de nuestras herramientas en su mantenimiento quedan expuestas y aparecen nuevas soluciones de diseño;

I have been arguing for some time that Caliper should be used as a data interoperability exchange standard between apps that operates through the LTI window

Diseñar, por lo tanto, se convierte en una práctica crítica localizada, que vincula la dimensión abierta (open source) de la tecnología con la práctica cultural del diseño.9Como lo anuncia un reciente texto sobre metodologías de diseño, este tiene lugaren términos de conocimientos, contextos, acciones, y aprendizajes situados, ya no neutrales ni universales (Simonsen et al. 2014). A partir de este debate es importante destacar la relevancia que estos diseñadores dan a las preguntas sobre el lugar, la localidad y la comunidad en su revisión de la práctica del diseño como un correctivo a la aceptación acrítica de las tecnologías digitales móviles y como una manera de redefinir su papel en la vida cotidiana.

La buena noticia, sin embargo, es que ya están sucediendo cosas más allá de lo usual en muchos ámbitos sociales, políticos y tecnológicos (como veremos más adelante, las transiciones civilizatorias ya están surgiendo); la mala noticia es que quizás no están sucediendo con la rapidez suficiente, si nos atenemos a los criterios de científicos y activistas del cambio climático

el metarrelato del ‘razonamiento abstracto’ del conocimiento ignora una característica muy importante de la producción de conocimiento que el pensamiento de diseño no olvida: el hecho de que la creación es siempre emergente, en los dos sentidos del término, es decir, auto-organizada y ‘alter-organizada’. Este último calificativo significa que el académico/diseñador también establece elementos y toma decisiones que permiten que la dinámica de auto-organización despegue y haga su trabajo.

co. El diseño es ontológico porque cada objeto, herramienta, servicio o, incluso, narrativa en los que está involucrado, crea formas particulares de ser, saber y hacer

big data solutions for cardiovascular and oncological research.

patient adherence

reliability and accessibility of big data will help facilitate increased reliance upon outcomes-based contracting and alternative payment models.

. Skill, effort and practice are regarded necessary elements in the process by which an actor becomes taken-for-granted (Bourdieu 2000). Accordingly, legitimacy is not simply out there for the asking, but has to be created as well as exploited by actors who seek to gain legitimation.

t from day one the Club complemented its hacks with outward-oriented com-munication aimed to make the hackers’ findings comprehensible and its political demands visible to the largest possible public. The Btx hack itself, for example, would not have been overly effectual if news media had not picked up the story. As news media reported widely on the hack and were largely in support of the hackers’ criticism, the hack gained an event character. Following the Btx hack, the CCC was recognized as a collective actor that had something relevant to say about the communi-cation and information landscape in Germany. The CCC was invited to speak on the main television news magazine of public broadcaster ZDF, the advice of Club members was frequently sought by national newspa-pers, they were asked by corporations to speak on data security and were requested by the newly established Green Party to write a report on the Party’s potential use of networked computing. one of the important details here is that instead of only being the subject of media coverage, the CCC had the opportunity to communicate its point of view to differ-ent audiences

a lower number of partici-pating members also meant a lower number of differing opinions; which, in turn, enabled the group to keep the frames of relevance more focused and to make decisions in a timely manner. Accordingly, performing direct digital action in the form of hacking was directly related to com-municative practices, as they later played an important role in relation to organizing, coordinating and executing the Club’s political project

Statistical and integrative system-level analysis of DNA methylation data

Projects by IF is a limited company based in London, England. We run this website (projectsbyif.com) and its subdomains. We also use third party services to publish work, keep in touch with people and understand how we can do those things better. Many of those services collect some data about people who are interested in IF, come to our events or work with us. Here you can find out what those services are, how we use them and how we store the information they collect. If you’ve got any questions, or want to know more about data we might have collected about you, email hello@projectsbyif.com This page was published on 25 August 2017. You can see any revisions by visiting the repository on Github.

Mount St. Mary’s use of predictive analytics to encourage at-risk students to drop out to elevate the retention rate reveals how analytics can be abused without student knowledge and consent

An institution has implemented a learning management system (LMS). The LMS contains a learning object repository (LOR) that in some aspects is populated by all users across the world  who use the same LMS.  Each user is able to align his/her learning objects to the academic standards appropriate to that jurisdiction. Using CASE 1.0, the LMS is able to present the same learning objects to users in other jurisdictions while displaying the academic standards alignment for the other jurisdictions (associations).

Moving to a Web of Linked Data for Credential Ecosystems

HarvardX Biomedical Data Science Open Online Training

Thismaynotbestatedexplicitly,butthediscourseoninscribingdigitalrightsashumanrightsininternationalhumanrightslawoftenassumesthattheforceofsuchlawswillguaranteethatindividuals,states,corporations,andotherbodieswillperformthem.Thisassumptionfailstorecognizethathowpeopleexperiencebeingdigitalcitizensandhowtheyperformrightsbybringingthemintobeingthroughenactingthemselvesarethegroundsonwhichrightswillbeguaranteed.Howpeopleexperienceperformingrightsisthekeytounderstandinghowtheyinhabitthatspaceofrightsanddevelopapoliticalsubjectivitynecessarytomakingrightsclaims‘I,we,theyhavearightto’.Withoutsuchunderstandingandwithoutdevelopingconceptsandmethodsappropriatetosuchanunderstanding,mosteffortstoinscriberights,weareafraid,wouldremaininadequate,forthesewouldberightswithoutpoliticalsubjects.Conversely,thesamecanbesaidforthosewhoassumethattheenactmentofrights,ofimaginativelyandperformativelybringingrightsintobeing,wouldguaranteetheirinscription.Withoutunderstandingthelegalityofclaimsandtheirscopeorsubstance,suchenactmentswouldremaininadequate,too.Withouttheforceoflaw,thiswouldamounttosubjectswithoutpoliticalrights.Itisthatspacebetweeninscriptionandenactmentthatprovidescluestounderstandinghowdigitalcitizensareemergingaspoliticalsubjectsofoureraandthosewhosepoliticssidewitheitherinscriptionorenactmentaresomewhatmissingthesignificanceofthatrelation.

First,itisundecidablewhetheranactisindeedcapableofproducingasubjectthatitnames.Forthatreason,withoutguarantees,itmustbedoneregardlessofitsactualeffects,fortheeffectsofcitationanditerationareasmuchaboutbringingthepoliticalsubjectthusnamedintobeingasaboutmakinganattempttoremindourselvesthat‘I,we,theyhavearightto’mustbeperformed.Second,withoutnamingthepoliticalsubject,withoutcitinganditeratingyetagainthat‘I,we,theyhavearightto’,itseventualeffectwillnotbeaccomplished,thatis,bringingtheforceoflawintobeing.

Thecharterincludesnineteenrightsandonesetofduties,whichareimportanttooutline

Rancièrecallsbringingthesetwoaspectsofrightstogetherasdissensus.Itisdissensusratherthanconsensusbecausepoliticsisalwaysacontestationoverwhoiscountedandwhatcounts.ForRancière,apoliticalsubjectinvolvesthecapacityforstagingsuchscenesofdissensus.Thus,‘politicalsubjectsarenotdefinitecollectivities.Theyaresurplusnames,namesthatsetoutaquestionoradisputeaboutwhoisincludedintheircount.’

Kroton, the country’s largest university, has about 2 million students

Open Referral : faciliter l’accès aux services sociaux pour les personnes dans le besoinLa rencontre a permis aussi de s’intéresser à des standards plus « grassroots », élaborés par la société civile en fonction de ses besoins. C’est le cas d’Open Referral, un standard élaboré aux Etats-Unis par Greg Bloom pour répondre à un problème essentiel : les personnes dans le besoin ont le plus grand mal à trouver les informations sur les services sociaux à leur disposition. A l’heure actuelle, tous les services d’aides sociales constituent leurs propres bases de données et se plaignent de leur incomplétude. Pour y répondre, Open Referral propose un standard de données pour déterminer : quel organisme propose quel service social ? Où, quand et comment y accéder ? Plutôt que de proposer une application qui répond à ces questions, Open Referral tente de développer un écosystème autour de ses données pour que les personnes dans le besoin trouvent l’information, quel que soit le service dans lequel elles cherchent. A Chicago, Open Referral a permis le lancement de plusieurs services autour des données ouvertes par Purple Binder, un annuaire des services sociaux. Le standard en est à ses débuts mais, pour Open Referral, l’incertitude porte sur le modèle économique : comment générer des revenus tout en augmentant l’impact social par l’ouverture des données ?

differentplatformsproducedifferentsociotechnicalarrangements—rules,norms,procedures,protocols,code,andalgorithms—inordertocreateaneffectivepresentationfortheirusers.Thesearrangements,orwhatwecallconventions,accomplishnotonlyasenseoffairnessbutalsostronghierarchiesthroughwhichnewsisproduced.[19]Thereisnoreasontobelievethattheseplatformsfeatureflathierarchiesorsmoothspacesofdialogue;rather,theyarehierarchicalandstriated.Theissuesofstatusandgatekeepingareasprominentintheseplatformsastheyareelsewhere.

‘citizenjournalismunderstandspeopleashavingpoliticalroles,interestsandrelationships,andasactivelyinterestedinsharingnewstheydeemrelevant.Itunderstands,orperhapsintuits,thataknee-jerkdefinitionofallformsofjournalismasacquiringanddistributinginformationmissesthepoint.’

‘themorepredictableresultwouldbeagradualdesertificationoftheculturallifeofindividualsnolongerabletoencounterwhatisunusual,unexpected,andsurprising.’[61]Ratherthanindividualizedbubbles,sharingsegregatessocialnetworkusersintoculturalbubblesofpreferences,products,andknowledge

Whileitmaysoundcontradictory,evenpersonalizationisanormalizingaction.Thepersonalizedprofilesor‘filterbubbles’generatedbyplatformssuchasGoogleandFacebookoperateonstandardizedalgorithms;whilethepredictionsabout‘whoyouareandwhatyou’lldoandwantnext’maybeuniquefordifferentsubjects,theyareuniqueonlyinlinewiththerulesofthealgorithm.

Thesedigitaltracesareoftenreferredtoasbigdataandarepopularlydiscussedasaresource,arawmaterialwithqualitiestobeminedandcapitalized,thenewoiltobetappedtospureconomies.Throughavarietyofpracticesofvaluation,corporationsnotonlyexploitthedigitaltracesoftheircustomerstomaximizetheiroperationsbutalsosellthosetracestoothers.Forthatreason,citizensubjectswhouseplatformssuchasGooglearesometimesreferredtonotasitscustomersbutasitsproduct.

atrendthatshecallsa‘modernexampleofatragedyofthecommons.’[33]Justasintheclassicversion,subjectswhoprotecttheirdatacontinuetoreapthecollectivebenefits(suchasinmedicine)ofdataleftinthecommons,yetthosebenefitsarethreatenedandwilldegenerateasdatasubjectsoptout.Othersstrikeadifferentwarning,arguingthattheanonymizationofidentityleadstocrowdbehaviourandsubjectstakinglessresponsibilityforwhattheysayanddoandincreasesthelikelihoodoftheirmisbehaving.[34]Boththeargumentsforandagainstanonymizationasasolutionpresumethatwhatisatissueisprotectingthe‘datadoubles’ofcitizensubjects.

digitaltracesalsogetcirculatedandrepurposedandinthisregardhavesociallivesbeyondthefeedbackloopsoftheirplatforms.[28]Thus,notonlyistrackingincipienttothefunctioningofspecificconventions,butthedatageneratedhasextensitysuchthatitcantravelbeyondandbetweenconventions.

TarletonGillespiearguesthattensionsbetweenusersandthedesignersoftheTwitteralgorithmarepartoflargerstakesinthe‘politicsofrepresentation.’Itisatensionunderscoredbyaconflictbetweenpeople’swilltoknowandbevisibletoothersandTwitter’simperativetodrawnewusersintonewconversations.Butsignificantly,Gillespienotesthatsuchalgorithmsnotonlyarebasedonassumptionsabouttheimageofapublictheyseektorepresentbutalsohelpconstructpublicsinthatimage.Thesamecouldbesaidofotherplatforms

Inthisinstance,criticsprotestedthatTwitterwasinvolvedincensoringpoliticalcontent,butothershaveshownthatthecomplexalgorithmsoftheplatformorganizeandfiltercontentinwaysoftenbeyondtheintentionsoftheirdesigners.Ratherthanasimplemeasureofpopularity,thealgorithmisbasedonacombinationoffactors,andthosethatTwitterhasrevealedincludeidentifyingtopicsthatareenjoyingasurgeinaparticularway,suchaswhethertheuseofatermisspiking(acceleratingrapidlyratherthangradually),whetherusersofatermformatightsingleclusterorcutacrossmultipleclusters,whethertweetsareuniqueormostlyretweetsofthesamepost,andwhetherthetermhaspreviouslytrended

platformssuchasGoogleandFacebookthatoperatelike‘predictionengines’by‘constantlycreatingandrefiningatheoryofwhoyouareandwhatyou’lldoandwantnext’basedonwhatyouhavedoneandwantedbefore

Popularlyknownasthe‘quantifiedself’,datatracesproduceacompulsiontonotonlyself-trackbutsharethisdatasothatsubjectscanmonitorthemselvesinrelationtoothersbutalsocontributetoresearchon,forexample,healthconditions.Ironically,whilegovernmentprogrammesforsharinghealthdatahavebeenscuppered,thesharingofhealthinformationthroughprivateorganizationssuchas23andMe(DNAprofilingofmorethan700,000members)andPatientsLikeMe(healthconditionsofmorethan250,000members)areproliferatingandpromotingdatasharingforthepublicgoodofadvancingmedicine.[62]Governmentsandcorporationsalikecalluponcitizensubjectstosharedataaboutthemselvesasanactofcommongood.Throughdisciplinarymethodstheycompelcitizensubjectstoconstitutethemselvesasdatasubjectsratherthanmakingrightsclaimsabouttheownershipofdatathattheyproduce.

Toputitbluntly,fromourperspective,popularcriticshavebecometooconcernedaboutcyberspacecreatingobedientsubjectstopowerratherthanunderstandingthatcyberspaceiscreatingsubmissivesubjectsofpowerwhoarepotentiallycapableofsubversion.

Or,aswewouldputit,actingthroughtheInternetandmakingconnectionswithothersdoesnotreplace,displace,orsupplantotherwaysofactinginsocialorculturalspacesinwhichweareembedded.

Howcanthecallingtoparticipatethatwehaveidentifiedproducedigitalcitizenswhoseactsexceedtheirintentions?Toputitdifferently,atensionexistsbetweenthewaysinwhichthefigureofthedigitalcitizenisconceivedinhegemonicimaginariesandlegaldiscoursesandhowsheisperformativelycomingintobeingthroughactionsthatequiphertobeacitizeninwaysthatarenotacknowledgedoralwaysintended.

Mossbergeretal.,forexample,understanddigitalcitizenshipastheabilitytofullyparticipateinsocietyonline,whichrequiresregularaccesstotheInternet,withadequatedevicesandspeeds,technologicalskillsandcompetence,andinformationliteracy.[21]Equippingthusincludesnotonlyhardware,suchasinstallingcomputersinclassroomsandlibrariesandexpandinghigh-speedbroadbandservices,butalsodevelopingskillsandcapabilitiesthroughtrainingcoursesincomputing,coding,andprogramming

Theempoweringpossibilitiesofaccessingandworkingwithdataalsounderpin‘opengovernmentdata’programmes.Opennessisextendedtomakinggovernmenttransparentthroughapublicrighttodataandfreedomtoinformation,aversionthatisalsoadvancedbycivicorganizationssuchasmySociety.[19]Thesecallforthanimaginaryofcitizensasdataanalystsequippedwiththeskillsnecessarytoanalysetheircommercialtransactionsandthusmakebetterdecisionsortoanalysethetransactionsofgovernmentsandthusholdthemtoaccount.

Inequalityisexpressedasleadingtotwodivisions:betweenthosewhodoanddonothaveaccessandbetweenthosewhodoordonotcontributetocontentorleavedigitaltraces.

Wecannotsimplyassumethatbeingadigitalcitizenalreadymeanssomething,suchastheabilitytoparticipate,andthenlookforwhoseconductconformstothismeaning.Rather,digitalactsarerefashioning,inventing,andmakingupcitizensubjectsthroughtheplayofobedience,submission,andsubversion

‘Theforceoftheperformativeisthusnotinheritedfrompriorusage,butissuesforthpreciselyfromitsbreakwithanyandallpriorusage.Thatbreak,thatforceofrupture,istheforceoftheperformative,beyondallquestionoftruthormeaning.’[22]Forpoliticalsubjectivity,‘performativitycanworkinpreciselysuchcounter-hegemonicways.Thatmomentinwhichaspeechactwithoutpriorauthorizationneverthelessassumesauthorizationinthecourseofitsperformancemayanticipateandinstatealteredcontextsforitsfuturereception.’[23]Toconceiveruptureasasystemicortotalupheavalwouldbefutile.Rather,ruptureisamomentwherethefuturebreaksthroughintothepresent.[24]Itisthatmomentwhereitbecomespossibletodosomethingdifferentinorbysayingsomethingdifferent.

We also downloaded Twitter user profiles, such as the size offollowers, along with their profile description.

We deleted non-directed tweets, but itshould be acknowledged that non-directed tweets may also bear implications for knowledgesharing and can be examined in future studies. The data cleaning procedure also excludedretweets and tweets that serve as quoting.

A Python script was used to download tweets through Twitter API.