Teachers tackling in/equities: understanding, recognition, and action
Bibliographic record
Abstract
Teachers’ equity work is often discussed in research literature, however, little empirical evidence exists about the nature of this work. This article explores teachers’ understandings, recognitions of, and actions regarding in/equities in their schools. Data was derived from interviews with fifteen urban elementary school teachers who engage in equity work. While researchers acknowledge that understanding equity and diversity plays a key role in preparing teachers to tackle inequities in their schools, relatively little is known about this process. Findings illustrate that although all participants share a common commitment to teaching for equity, they held differing ideas about the meaning of equity, what equity looked like, and what their role should be for redressing inequity. Participants understanding of in/equity where found to exist on a spectrum from less to more developed, as were their actions. Findings also illustrated inconsistencies with regards to where participants existed on the continuum of understanding in relation to their actions. The nature of participants’ level of understanding, how they described their recognitions of in/equity, and the actions they took to address inequities are described thematically to illustrate their general nature as there is little information available in particular regarding teachers’ understandings and recognitions of inequity
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.026 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| 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".