Teachers’ Enactment of Equity in Alternative Schools: A critical discourse analysis
Bibliographic record
Abstract
Purpose: Understanding the needs of at-risk students is thought to be an essential element for educators when they formulate and design alternative educational settings. Yet, it may be difficult for educators to distinguish between responding to the needs of an at-risk student and providing educational equity. The researcher applied equity principles and policy implementation literature to explore how alternative school teachers of at-risk students define, interpret and enact equity. Research Methods: This research was designed as a qualitative cross-case study focusing on five alternative schools in California and Texas. A qualitative thematic analysis was first applied to interviews from fifteen alternative school teachers, followed by a critical discourse analysis of government artifacts, and discursive and social practices. Findings: Teachers’ enactment of equity equated to opportunity; at-risk students were afforded equity by virtue of enrollment in the schools. Implicitly, teachers acted as gate keepers to their classroom and as such only certain students were entitled to attend. Equity arguments emerged when external forces were perceived as creating inequities. Implicit equity arguments emerged by how teachers defined success. Implications: Innovative design and practices used in alternative schools are insufficient for ensuring equity. Enactment of equity through pedagogical choices is reduced by policy procedures. Research is needed in areas: a) that will help teachers reflect on their values and priorities for at-risk students and b) of how efficacy is measured for the alternative school.
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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 | 0.039 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.012 | 0.030 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".