Introduction to the Special Issue on Urban Teacher Residencies
Bibliographic record
Abstract
Despite the rapid expansion of and investment in urban residency programs, a key tenet of the residency model—that they prepare teachers for targeted urban settings—remains largely unexamined. Although some might argue that a “good teacher” can transcend contexts—we ask in this issue whether there may be particular features of the setting or context that are important for new teachers to learn about. In the papers in our special issue, the authors examine more closely what kind of preparation may be necessary for specific contexts. This themed issue features scholarship that examines efforts to prepare teachers for clinical practice in particular contexts. The articles share evidence from three residency programs (each engaged in systematic data collection) on opposite sides of the US to point to features of the context that may matter for teaching; the design of opportunities to learn in these programs; and data that sheds light upon these questions. Given recent findings about the strong retention of graduates of ‘context-specific programs’ these examinations not only provide insight into the promise of urban residency programs but also serve as a call for programs to be epecially clear about the specific features of the setting that may matter for teaching.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.049 | 0.016 |
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".