Bibliographic record
Abstract
This article explores teachers’ reasons for engaging in equity work. Although multiple bodiesof literature discuss teaching for equity from different perspectives, little empirical data existsabout what informs or motivates people to teach for equity. This study aims to help fill thatgap in existing research with the purpose of informing professional development and trainingfor practicing teachers hoping to or attempting to engage in equity work, and for encouragingand motivating other educators in beginning to engage in equity work. Data was gatheredthrough interviews with 15 teachers from three large school boards in Southern Ontario.Findings illustrate that participants were motivated to engage in equity work because ofpersonal experiences with inequity, witnessing other people experience inequities, and learningabout inequities in school. Three key findings stand out with regards to their utility forprofessional development and training: all participants spoke of critical incidents whichcompelled them to do equity work; emotional struggles were associated with their work, yetthey remained hopeful in the possibility of change regardless of what they had experienced;finally, the nature of the equity work that participants chose to undertake was directly relatedto the nature of their experience with in/equity.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".