Bibliographic record
Abstract
This special themed issue includes a selection of articles that emerged from a larger qualitative, ethnographic study of multilingual children’s identity construction, identity politics and cultural positioning in heritage language contexts in Montreal, Quebec, Canada. The McGill Multilingual Literacies Research team formed in 2000 is like a microcosm of the very phenomena of multilingualism under consideration. Most team members are people who were multilingual from earliest childhood, went to and/or taught in heritage language contexts and are aware of the nuanced meanings of their particular contexts. Our ‘coming together’ occurred naturally out of a shared interest in the experiences of multilingual students, multiple languages and literacies in a province where French is spoken by a large majority of the population and a city where communities speak in languages from all over the world. Although we have since become more consciously aware of Montreal as a unique space for understanding identity politics and multiple languages (Maguire, Beer, Attarian, Curdt-Christiansen and Yoshida 2005), we were only tacitly aware of its significance in our initial research endeavours. Indeed we were engaged and immersed in multilingual research without knowing how to do it or that we were consciously doing it! (Roth 2006).
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.504 | 0.275 |
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; the direct Gemma label and the distilled Codex classifier 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".