Countering a ‘Back-to-Basics’ Approach to Teacher Education: Multiliteracies and On-Line Discussions in a Community of Practice
Bibliographic record
Abstract
Aiming to extend sociocultural theory about literacy education in teacher programs, this article reports on results from a qualitative study conducted in a Western Australian university. The project tracked a group of initial teacher and graduate education students collaborating in on-line discussion embedded in a literacy course. The article focuses on how one pre-service teacher constructed situated identities and understandings about literacy as she interacted on-line with peers and the course instructor in a community of practice (Lave & Wenger, 1991). Suggestions are provided for designing on-line CoPs that consider power and an expanded definition of literacies.
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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 | 0.047 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.023 | 0.104 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.004 | 0.032 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".