“I feel invisible sometimes”: The manifestations and effects of racism in social media narratives from all-girls’ schools in urban areas of central Canada
Bibliographic record
Abstract
2020 saw a rise of social media accounts dedicated to sharing BIPOC students’ experiences with racism at prestigious secondary or post-secondary institutions. Through a narrative analysis of one such account, this study attempts to identify the manifestations of racism in all-girls’ independent schools in urban areas of central Canada. It was found that racism expressed by fellow students tends to be exclusionary, and deprives BIPOC of community and social acceptance; racism expressed by staff and faculty is often dismissive in nature, and damages BIPOC’s faith in their teachers’ ability to provide them with academic and emotional support; and institutional racism limits BIPOC students’ ability to communicate their needs at an administrative level, and denies them the safe and inclusive learning environment they expect their institutions to provide. All three forms of racism stem from the silencing and neglect of BIPOC student voice, which suggests that the prioritization of BIPOC student voice might help address and mitigate racism in these institutions.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.030 | 0.020 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".