Text Production as Process: Negotiating Multiliterate Learning & Identities
Bibliographic record
Abstract
In this secondary research study, we investigate the text/identity/curriculum work enacted in a primary university-school project with third-grade children in Québec who were engaged in inquiry into children’s rights through bilingual text production. Drawing on sociocultural perspectives of language and identity as well as translanguaging, we examined both the product and the process of identity text production. Analysing classroom interactions and children’s bilingual production using discourse analysis, the findings show how teachers’ cross-curricular efforts in creating translanguaging spaces and shifts with students’ emerging bilingualism and critical understanding of children’s rights issues provided spaces for identity and knowledge re/construction, effectuating new curricular opportunities, inquiries, and language/literacy learning. This process-oriented view of identity text production points to the mutually constitutive nature of identity/text/curriculum work, inviting a dynamic, non-linear understanding of text production, and calling for reflexive attention to power relations in classroom interactions for greater possibilities for meaningful identity and knowledge making.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".