How Cooperating Teachers and Interns Understand “Teaching for a Better World” During Internship
Bibliographic record
Abstract
This study utilized a descriptive questionnaire to determine how interns and cooperating teachers translate the faculty’s expectations for teaching for social justice into practice during internship. The following research questions were formulated to guide the study: what are the similarities and differences between the intern’s and cooperating teacher’s receptiveness to teaching for social justice during in internship? And, how do interns and cooperating teachers differ in their perception of being controversial and integrating world views and perspectives in content and instructional approaches during internship? The participants included 142 cooperating teachers and 54 interns. Just over half of the cooperating teachers described their interns as either rigorously or actively finding some opportunities to teach for social justice. And, even though over a third of the interns reported that they were either rigorously or actively integrating some opportunities, it is notable that fewer interns than cooperating teachers were certain that they were teaching for social justice. The site of greatest tension between interns and cooperating teachers appeared to be in relation to discussing personal biases and what it means to be intentionally controversial.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".