34 Matching Annotations
  1. Feb 2025
    1. Average score increased in mathematics at grade 4; no significant change at grade 8. Average scores declined in reading at both grades

      This is a significant finding that scores are not increasing in 4 grade levels for math and reading scores declining?

    1. At the end of fifth grade, about 13% of White students and 22% of Asian students displayed advanced math-ematics achievement. The contrasting percentages were 2% and 3% for Black and Hispanic students, respectively.

      This is definitely a racial disparity and is an important finding to discover that more needs to be done in or before kindergarten to positively impact science, math, and reading achievements as a strong explanatory factor.

    2. Family TV rules (α= .52) was a standardized composite of three parent-reported binary questions indicating whether there were family rules about: (a) allowable TV programs; (b) how many hours of TV the child could watch; and (c) how early or late the child watched television. Parents volun-tarily self-reported information on their parenting practices. Similar groupings of these items have been used in prior work investigating parental literary activities (e.g., Byrnes et al., 2019), cognitive stimulation (e.g., Slicker et al., 2021), parent-child activities (e.g., Kim, 2021), parental warmth (e.g., Ogg & Anthony, 2020) and family TV rules (e.g., Morgan et al., 2021). Relatively low values of alpha for emergent literacy and family TV rules were likely due to the small number of scale items.

      I am surprised to find later in the paper that these questions were not significant to any grade.

    3. School Characteristics. School opportunity factors as continu-ous variables included the percent of students receiving free school lunch and reduced-price school lunch, the percent of non-White students, and averaged science and mathematics achievement in the spring of kindergarten

      I feel like there are definitely some additional measures that could be included here, also the practice of factoring all non-white students into one group in a racial and ethnic disparity study is interesting... If this is the way the survey data was conducted prior to their study, they could state that here or in the limitations, but they do not so you are left wondering why this choice was made.

    1. ew studies, however,have examined the impacts of systemic sexism and racism onchemistry student outcomes1despite the ACS and many otherleading bodies in the STEM disciplines calling for an increasedeffort to improve equity for women and Black, Indigenous, andpeople of color (BIPOC).2−6The lack of fundamental researchon inequities in chemistry student outcomes makes it difficultfor instructors and researchers to contextualize inequities intheir student outcome data

      Unfortunately, this is the case in most research areas, such as women's health research.

    2. Society would have toprovide opportunities equivalent to taking the course up to two and a half times to repay the largest educational debts.

      This sentence definitely drives home the importance of this study and a call to action.

    1. Chicago Public Schools spends about $8,482 per pupil while nearby Highland Park spends $17,291 per pupil. Chicago Public Schools have an 87% Black and Latino population while Highland Park has a 90% White population. • Per pupil expenditures in Philadelphia are $9,299 for its 79% Black and Latino population while across City Line Avenue in Lower Merion the per pupil expenditure is $17,261 for a 9 1 % White population. • New York City Public Schools spends $11,627 per pupil for a student population that is 72% Black and Latino, while subur

      I am not saying this is inaccurate, but it would be nice to have additional context here. I can't help questioning could some of the differences be based on COL/expenses in those areas? (i.e., The cost of living in Chicago, Illinois is 16.4% cheaper than Highland Park, Illinois).

    1. educational data are a key element in efforts to support educational equity

      Yes. However, I am concerned that districts were identified as not complying with the requirements and monitoring responsibilities and some were 'engaged in unlawful practices to delay or deny student identification. I know it says some are engaged in practices addressing compliance issues now, but what ensures quality identification continues to occur long-term aside from standards that can be easily ignored for years...public education data should be required for all districts and states and be transparent and regularly reviewed for this type of issue.

    2. This material is based upon work supported by the NationalScience Foundation under Grant No. 1661097 and Grant No. 1740695.

      The recent federal funding issues makes me worry these types of studies will suffer funding or not occur at all due to talk of banning a lot of the topics discussed herein.

    3. call for a shift toward greater use of data in educator preparation programs.

      I enjoy when authors use their abstract to a call for action right away on something like this. The intention is already clear.

    1. so-called recidivism models.

      There are flaws in many risk-assessment tools (risk to reoffend/recidivate). Even tools that are validated for certain populations exclude mental health, trauma, and societal factors and can lead to inaccurate risk assessments. Additionally, many community supervision officers who administer the tools, have authority to 'override' risk scores...

    2. Upon meeting her ayear later, they can suffer a few awkward hours because their models areout of date. Thomas the Tank Engine, it turns out, is no longer cool. Ittakes some time to gather new data about the child and adjust theirmodels

      This is a great example of why models need constant updates when looking at human behaviors.

    3. simplifying the world into a toy version that can beeasily understood and from which we can infer important facts andactions. We expect it to handle only one job and accept that it willoccasionally act like a clueless machine, one with enormous blind spots

      "...simplifying the world into a toy version that can be easily understood..."

    4. Is that really amodel?The answer is yes. A model, after all, is nothing more than an abstractrepresentation of some process, be it a baseball game, an oil company’ssupply chain, a foreign government’s actions, or a movie theater’sattendance. Whether it’s running in a computer program or in our head,the model takes what we know and uses it to predict responses in varioussituations. All of us carry thousands of models in our heads. They tell uswhat to expect, and they guide our decisions

      I think people have high expectations for 'models' or 'tools' thinking they are all validated and rigorously tested, when in fact, few are.

    1. The next week, Bellenson renamed the app ‘122 Shades of Gray’ and added a note explaining that the authors of the Science study weren’t affiliated with the project. He says that because the app has always warned users that it is not predic-tive, it does not misrepresent the study.

      This walk-back of the title and removing the affiliates is an obvious acknowledgement of doing the wrong thing. This app is dangerous, especially given Uganda's current laws towards the LGBTQIA+ community.

    2. the average black person was also substantially sicker than the average white person

      How many years of data do we need to have to show the differences in race, ethnicity, and gender healthcare issues to make the necessary adjustments in how we approach these issues as a society? It's really sad that we don't have more supportive funding and projects towards this in 2025.

  2. Jan 2025
    1. Examine power. Data feminism begins by analyzing how power operates in the world..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1EMILY JOSEPHChallenge power. Data feminism commits to challenging unequal power structures and working toward justice.Elevate emotion and embodiment. Data feminism teaches us to value multiple forms of knowledge, including the knowledge that comes from.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }11 people as living, feeling bodies in the world.Rethink binaries and hierarchies. Data feminism requires us to challenge the gender binary, along with other systems of counting and classification that perpetuate oppression..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Eva Maria ChavezEmbrace pluralism. Data feminism insists that the most complete knowledge comes from synthesizing multiple perspectives, with priority .d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }3Eva Maria Chavez, Fagana Stone, Tegan Lewisgiven to local, Indigenous, and experiential ways of knowing.Consider context. Data feminism asserts that data are not neutral or objective. They are the products of unequal social relations, and this context is essential for conducting accurate, ethical analysis..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Natalie Pei XuMake labor visible. The work of data science, like all work in the world, is the work of many hands. .d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Melinda RossiData feminism makes this labor visible so that it can be recognized and valued.

      Flagging this future reference.

    2. There are also, always, people who go uncounted—for better or for worse.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }11. And there are problems that cannot be represented—or addressed—by data alone. And so data feminism, like justice, must remain both a goal and a process, one that guides our thoughts and our actions as we move forward toward our goal of remaking the world..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }111

      This is really important. Many organizations collect data, but how it is USED is just as important.

    3. That Darden and Champine were able to view their work as a success despite these inherent constraints underscores even more the importance of listening to and learning from people whose lives and voices are behind the numbers. No dataset or analysis or visualization or model or algorithm is the result of one person working alone. Data feminism can help to remind us that before there are data, there are people—people who offer up their experience to be counted and analyzed, people who perform that counting and analysis, people who visualize the data and promote the findings of any particular project, and people who use the product in the end

      "No dataset.... is a result of one person working alone." = so important to acknowledge.

    4. Throughout her career, in ways large and small, Darden used data to make arguments and transform lives. But that’s not all. Darden’s feel-good biography is just as much a story about the larger systems of power that required data—rather than the belief in her lived experience.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Cynthia Lisee—to perform that transformative work. An institutional mistrust of Darden’s experiential knowledge was almost certainly a factor in Champine’s decision to create her bar chart. Champine likely recognized, as did Darden herself, that she would need the bar chart to be believed..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }11

      Using data to transform lives = power of data.

    5. Underlying data feminism is a belief in and commitment to co-liberation: the idea that oppressive systems of power harm all of us, that they undermine the quality and validity of our work, and that they hinder us from creating true and lasting social impact with data science..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1nyah bean

      I wish more people understood this.

    6. These were the people who provided her with community and support.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Melinda Rossi—and likely a not insignificant number of casserole dinners—as she ascended the government ranks. These types of collective efforts have been made increasingly legible, in turn, because of the feminist scholars and activists whose decades of work have enabled us to recognize that labor—emotional as much as physical—as such today.

      I feel this level of community is necessary for those of us ethically reporting data to avoid it being ignored or not acted upon.

    7. Darden consulted with Langley’s Equal Opportunity Office, where a white woman by the name of Gloria Champine had been compiling a set of statistics about gender and rank

      Darden consulting with EOO was a brilliant use of data to impact change.

    8. We write as two straight, white women based in the United States, with four advanced degrees and five kids between u.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Naomi Hailus. We identify as middle-class and cisgender—meaning that our gender identity matches the sex that we were assigned at birth. We have experienced.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Jillian McCarten sexism in various ways at different points of our lives—being women in tech and academia, birthing and breastfeeding babies, and trying to advocate for ourselves and our bodies in a male-dominated health care system. But we haven’t experienced sexism in ways that other women certainly have or that nonbinary people have, for there are many dimensions of our shared identity, as the authors of this book, that align with dominant group positions. This fact makes it impossible for us to speak from experience about some oppressive forces—racism, for example. But it doesn’t make it impossible for us to educate ourselves.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Melinda Rossi and then speak about racism and the role that white people play in upholding it..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }11 Or to challenge ableism and the role that abled people play in upholding it. Or to speak about class and wealth inequalities and the role that well-educated, well-off people play in maintaining those..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; } Or to believe in the logic of co-liberation. Or to advocate for justice through equity. .d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1nyah beanIndeed, a central aim of this book is to describe a form of intersectional feminism that takes the inequities of the present moment as its starting point and begins its own work by asking: How can we use data to remake the world?

      Glad to read this acknowledgement of limitations paired with a call for equity discussions among peers.

    9. n other words, Friedan had failed to consider how those additional dimensions of individual and group identity—like race and class, not to mention sexuality, ability, age, religion, and geography, among many others—intersect with each other to determine one’s experience in the world.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1Jayri Ramirez. Although this concept—intersectionality.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }11—did not have a name when hooks described it, the idea that these dimensions cannot be examined in isolation from each other has a much longer intellectual history..d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }11

      Intersectionality is really important when you are addressing large populations of people such as 'women in the US'. It is important to see that by excluding a large portion of women, Friedan's book was considered not relevant to many.

    10. Most of the engineers, who were predominantly men, never even bothered to learn the computers’ names.d-undefined, .lh-undefined { background-color: rgba(0, 0, 0, 0.2) !important; }1111.

      The fact that NASA Langley allowed an environment wherein smart, qualified women were referred to as 'computers' and that it was acceptable for others not to learn/use their names highlights the fact that they were clearly intimidated by the level of work they were accomplishing, especially given the circumstances and limited equipment to do this work.

    11. Her newly minted master’s degree in applied math had earned her a position as a data analyst there.

      Later in the article, it indicates that men at NASA Langley with these math credentials were placed into engineering positions. This inequitable hiring/placement practice displays clear sexism right from the start.