Bibliographic record
Abstract
This article outlines a recent study on school culture and technology adoption. Adapting Hargreaves’ (2003) model of school cultures, research findings are presented on three schools involved in a study on teacher transformation using educational technology to explain how each school represents a separate school culture and school regime. Each school is profiled to demonstrate, through direct quotes from the participants, how a specific school culture or regime can reflect varying degrees of transformation, and subsequent technology adoption. Résumé : Cet article présente une étude récente portant sur la culture scolaire et l’adoption de la technologie. En utilisant une adaptation du modèle des cultures scolaires de Hargreaves (2003), les résultats de recherche de trois écoles qui ont participé à une étude sur la transformation des enseignants utilisant la technologie éducative sont présentés afin d’expliquer comment chaque école représente une culture d’école et un régime scolaire distincts. Chaque école est profilée dans le but de démontrer, au moyen de citations directes des participants, la façon dont une culture d’école ou un régime scolaire donné peut se traduire par divers niveaux de transformation et, conséquemment, d’adoption des technologies.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".