"I'm Not the Only Writer in The Room": A Framework for Co-Creating Confident Writing Classrooms
Bibliographic record
Abstract
This study is rooted in social cognitive theory, specifically Bandura's work on self-and collective efficacy. The authors explore self reported confidence levels with writing instruction from secondary teachers across subjects in Canada and the United States by pairing a self-efficacy scale developed by Locke and Johnston (2016) with semi-structured interviews conducted via Skype. 60 teachers participated in the survey, with 25 from Canada and 35 from the United States. Although teachers report relatively strong levels of self efficacy in writing instruction, the responses of participants regarding collective efficacy are more mixed. Based on these results, coupled with six interviews (split evenly between teachers in Canada and the United States), the authors propose a framework to help teachers of all subject areas increase their confidence in writing instruction while also helping students develop their own confidence as writers. This three-pronged framework of identity, context, and authority, relies on co-creating community with students. The potential of this framework is creative, offering teachers (and students) multiple ways into a conversation about writing that will not only enhance confidence, but will create a classroom culture in which diverse writing strategies and perspectives are valued.
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 | 0.022 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.019 | 0.055 |
| Scholarly communication | 0.020 | 0.013 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".