25 Matching Annotations
  1. Dec 2018
    1. For many years, academia has relied on citation count as the main way in which we measure impact or importance of research. As a result, citation count is one of the primary metrics used when evaluating researchers. Citation counts also form the basis for other metrics, most notably Clarivate’s Impact Factor as well as the h-index, which respectively evaluate journal quality/prestige and researcher renown.

      The metrics the Academy uses to measure "impact" are regressive.

  2. Nov 2018
    1. these are not the only reason behindthe trends observed. An examination of the peaks

      See http://wikicite.org/statistics.html for similar phases and events better analysed by history instead of calculations.

    2. Wikidata quality assessment

      I guess the the average values are spoiled by outliers. A look at distribution would be interesting instead of average and median value only.

    3. Ontology depth

      I'm curious about the depth distribution to compare my findings on other classification systems, including Wikipedia categories: https://arxiv.org/abs/cs/0604036

    4. sed in the present analysis

      I'd add number of classes with connected Wikipedia articles because these provide definition and context

    5. We used the following keywords:‘ontology metrics’, ‘ontology evaluation framework’, and ‘ontology evaluation’. From the results,we selected only papers including primarily structural metrics.

      A similar study on metrics and evaluation of classifications, taxonomies, thesaurus and other knowledge organization systems would be interesting!



  3. Nov 2017
    1. We calibrate the model for 6 countries at various stages of economic development: 3 low-incomecountries (Uganda in 2005, Kenya in 2006, and Mozambique in 2006), and 3 emerging marketeconomies (Malaysia in 2007, Philippines in 2008 and Egypt in 2007).

      Data & Calibration

    2. In the model, agents are heterogeneous { distinguished from each other by wealth and talent.Individuals choose in each period whether to become an entrepreneur or to supply labor for a wage.Workers supply labor to entrepreneurs and are paid the equilibrium wage. Entrepreneurs haveaccess to a technology that uses capital and labor for production. In equilibrium, only talentedindividuals with a certain level of wealth choose to become entrepreneurs.

      In this model, a heterogenous population is 'created' and differentiated by their talent and wealth. Only people with enough of both can be entrepreneur, otherwise they will stay as wage earners.

    1. The Solow–Swan model augmented with human capital predicts that the income levels of poor countries will tend to catch up with or converge towards the income levels of rich countries if the poor countries have similar savings rates for both physical capital and human capital as a share of output, a process known as conditional convergence.

      Income convergence of the poor and rich people will happen conditional on them enjoying similar savings rates. Otherwise, it might not happen.

    1. Maybe not a ELI5, but: Moments are expectations of things. E(X) is often called the "first moment," E(X2 ) is the "second moment," etc. They can also be more complicated, like E(exp(5x+y)), or whatever. In econometrics, you're trying to figure out something about the underlying distribution of your y's and your x's (and the errors). Often you don't know the shape of the distribution, but you know some moments of the distribution. This is useful because you can't use maximum likelihood estimation unless you make assumptions on the entire distribution. With ordinary least squares, you assume that E(ex) = 0, that is, the errors are uncorrelated with the regressors. You can write e = y - xbeta, to get a moment condition E(x(y-xbeta)) = 0. If you do GMM with this moment condition, you get the regular OLS estimator. If you have endogeneity of some kind, you don't know that E(ex) = 0, but you might have some instruments Z, such that E(ez) = 0. This gives you the moment condition E(z(y - x*beta)) = 0. GMM is nice because it makes relatively weak assumptions compared to other ways of estimating parameters. I hope that helps!

      GMM - I don't get it, at all. What are moments? How are they used? Why are they used? Thanks all!

  4. Sep 2017
    1. proportion of youth and adults with information and communications technology (ICT) skills, by type of skill’ (SDG 4.4.1)

      indicator of digital skills target

    2. Develop appropriate measurement and monitoring strategies

      Recommendation 4

    1. it shows the six degrees of separation.

      What SNA will help with is not only creating visuals of networks but providing metrics to be able to understand the different roles and positions each nodes plays in the network as well as comparing different network structures.

  5. Jul 2017
    1. 1000 assignments in the assignment bank and 11 thousand submissions

      ds106: syndicated: 1K assignments and 11K submissions

    2. The first great experiment was UMW Blogs, our institutional blogging system which debuted in fall of 2007. In that nine years, it has had almost 13,000 users and it now contains 11,000 individual WordPress sites.

      UMW blogs: 13K users, 11K blogs

  6. Feb 2017
    1. (Among the recommendations: Greater emphasis on visuals, greater variety of formats and voices. They also announced that the Times would be introducing an alternative metric to pageviews that would “measure an article’s value to attracting and retaining subscribers.”)

      How would they measure this exactly?

  7. Jan 2016
  8. Jul 2015
    1. I think it is possibly too early to tell.

      As Steven Hill of HEFCE recently suggested, it might be far better for UK institutions to reject these tables: "What if all UK institutions made a stand against global rankings, and stopped using them for promotional purposes? The reputation of the UK’s higher education sector would stand firm, and a really strong signal would be sent to the rest of the world. Not drifting, but steering purposely through the metric tide." http://blog.hefce.ac.uk/2015/07/08/the-metrics-dilemma/

    1. based on a scientific analysis of citation data

      JIF is discredited in many reviews. See for example http://dx.doi.org/10.3389/fnhum.2013.00291. A recent independent review of metrics for the Higher Education Funding Council for England also strongly recommended against the use of measures like JIF: http://www.hefce.ac.uk/pubs/rereports/Year/2015/metrictide/

  9. Oct 2014
    1. metrics on annotations/comments

      We should compare these asks with our own needs for monitoring our public service at Hypothes.is.

  10. Sep 2013