Writer Identity and the Teaching and Learning of Writing
Bibliographic record
Abstract
In the light of increasing international interest in teachers’ and students’ literate identities and practices, this book addresses the under-researched area of teachers’ and students’ writer identities and in so doing seeks to advance the field. The volume is premised upon two key assertions, namely that writer identity matters and needs recognition and development in educational contexts, and that young people’s writer identities are influenced by the ways in which their teachers identify as writers. It brings together new empirical studies and scholarly reviews on writer identity and the teaching and learning of writing from researchers working in Australia, Canada, Denmark, New Zealand, the UK and the USA. The volume explores what it means to identify as a writer, the issues which surround the concept of ‘being a writer’, and the consequences which arise when teachers and students do or do not identify as writers. Several contributors also conceive of and examine writing as a significant form of identity work.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".