Language, culture and identity in applied linguistics
Bibliographic record
Abstract
1. A Study on the Effects of Processing Instruction and Meaning Output-based Instruction on the Acquisition Italian Subjunctive of Doubt and Opinion Alessandro Benati, Old Royal Naval College, Greenwich 2. Revealing and Obscuring the Writer's Identity: Evidence from a Corpus of Theses Maggie Charles 3. Identity in a Francophone Cultural Context: Issues of Language Rights and language Use in Canada Maeve Conrick, University College Cork 4. Face in L2 Argumentative Discourse Psycholinguistic Constraints on the Construction of Identity Doris Dippold 5. Sami Languages: Between Hope and Endangerment Hannele Dufva and Sari Pietikainen, University of Jyvaskyla, Finland 6. Perceptions of Varieties of Spoken English: Implications for EIL Sue Fraser 7. Who or What is the Students' Audience?: Discoursal Construction of Audience Identity in Undergraduate Assignments Lynda Griffin 8. Language, Learning and Identification Roz Ivanie, University of Lancaster 9. Variation in Disciplinary Culture: University Tutors' Views on Assessed Writing Tasks Hilary Nesi and Sheena Gardner, University of Warwick 10. Interdisciplinarity and Writer Identity: Students' Views and Experiences Bojana Petriae 11. Identity Formation and Dialect Use Among Young Speakers of the Greek-Cypriot Community in Cyprus Andry Sophocleous
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".