Race, Education and #BlackLivesMatter: How Social Media Activism Shapes the Educational Experiences of Black College-Age Women
Bibliographic record
Abstract
The #BlackLivesMatter movement, which rose to prominence following the state-sanctioned murders of several unarmed Black Americans, shed light on the power of social media to serve as a platform for transformative resistance, counter-storytelling, and civic engagement for marginalized youth. With some of the highest rates of social media use to date, it is not altogether surprising that Black college-age youth, particularly young Black women, were the primary curators of the politicized social media storm that captured the nation’s attention and spurred a viral hashtag into a historical movement. While Black women’s persistent use of social media to enact resistance in their schools, in their communities and in popular media is indicative of its importance in their socio-academic experiences, there remains a substantial dearth in educational scholarship examining the nexus of race, gender and resistance as it relates to the digital realm. By drawing upon critical race theory in education (CRT), Black feminist thought (BFT), and Black Feminist Technology Studies (BFTS), this study centers the voices of 17 Black undergraduate women from eight universities across the US and Canada in an attempt to answer these research questions. Findings revealed the ability of social media to provide young Black women with a sense of safety, visibility, and community that is not regularly available in offline settings. In addition to the benefits of digital resistance, this study also illuminated a spectrum of unintended health impacts of reading, responding to and witnessing anti-Black violence online. A growing skepticism of internet technology to single handedly transform society also emerged from the findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".