<i>Equipping Educators for Equity Through Ethnic‐Racial Identity</i> Curriculum: Comparing Teachers' Fidelity of Implementation Across Remote and in‐Person Training
Bibliographic record
Abstract
Professional development (PD) to help teachers learn to use curriculum materials can be effective in aiding fidelity of implementation and supporting student learning. PD may be particularly necessary for curricula focused on students' ethnic-racial identities, given educators' potential discomfort and limited formal training focused on strategies for discussing race/ethnicity in class. The Equipping Educators for Equity through Ethnic-Racial Identity (E⁴) PD prepares educators to implement an eight-lesson ethnic-racial identity curriculum with high school students. We tested whether fidelity of implementation of the ethnic-racial identity curriculum varied by two training modalities: in-person versus remote. Teachers' (N = 14) fidelity of implementation across 55 classrooms was assessed via 440 observations. Teachers' fidelity regarding curriculum adherence was high (76%) and did not vary significantly by training modality. Remote and in-person training resulted in similar fidelity of implementation, suggesting remote trainings may enable scaling up without sacrificing impact.
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 | 0.011 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".