Identity within the mainstream grade 8 writing classroom: ways in which honouring identity enhances the teaching and learning of writing
Bibliographic record
Abstract
In today’s world, with increased movement and growing globalization, classrooms are alive with multiple languages and ethnicities. Many students live, perhaps unknowingly, with hybrid identities and many find written communication challenging. Writing, involving not only the mechanics but also the art (Graham and Perin, 2007), is a complex skill for every student to master, the first-language (L1) learner as well as the second- (L2). Likewise, identity, influenced by relationships, experiences, and context, is a complex phenomenon. Because of this complexity, identity is best understood through the sociocultural perspective (Gee, 2000-2001; Nelson, 2008; Bucholtz & Hall, 2005). Ethnographic research is able to bring to the forefront or make visible nuances that “through a wider analytic lens” often remain hidden (Bucholtz and Hall, 2005, p. 597). Thus, to further understand the interplay between identity and the teaching and learning of writing, an (auto) ethnographic approach is used.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| 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".