Understanding a Teacher’s Knowledge of Classroom Community (Comprendre la connaissance qu’une enseignante a de la communauté formée par sa classe)
Bibliographic record
Abstract
ABSTRACT. Using primarily online interaction, we worked with a grade one teacher to help her develop an understanding of community in her own classroom. Using an interpretive interactionist methodology, we theorized four domains in her view of classroom community: trust, membership, power, and capacity. The teacher’s perceived success in creating community suggested the development of an adaptive community coming from a newfound ability to negotiate the contradictions inherent in each domain. Comprendre la connaissance qu’une enseignante a de la communaute formee par sa classe RESUME. Par le truchement principal de sessions interactives en ligne, nous avons aide une enseignante de premiere annee du primaire a perfectionner sa comprehension de la communaute formee par ses eleves. Selon une methode interactioniste interpretative, nous avons elabore une theorie axee sur quatre volets d’interpretation de la communaute formee par la classe : confiance, appartenance, pouvoir et capacite. Le succes que percoit l’enseignante sur le plan de la creation d’un esprit communautaire suggere la mise sur pied d’une communaute adaptative issue d’une nouvelle capacite a traiter les contradictions inherentes a chaque volet.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".