Novice language teachers : insights and perspectives for the first year
Bibliographic record
Abstract
1. Insights and Perspectives for The First Year of Language Teaching (Thomas S.C. Farrell)2. Teachers' use of metaphor in making sense of the first year of teaching (Steve Mann)3. Trained for teaching high school, poached for teacher training: A case study of a Cambodian English teacher's first year of teaching in Cambodia. (Stephen Moore) 4. Learning to teach in the First Year: A Singapore Case Study (Thomas S.C. Farrell)5. Occupational socialization in the first year of teaching: perspectives from Thailand (David Hayes)6. The Transition from Teacher Education to ESL/EFL Teaching in the First Year for Non-Native English Speaking Teachers in Canada (Clea Schmidt )7. The Beliefs and Practices of Novice Teachers in Hong Kong: Change and Resistance to Change in an Asian Teaching Context (Alan W. Urmston and Martha C. Pennington)8. Teaching Post-CELTA: the interplay of novice teacher, course and context (Michela Borg)9. Ghosts on the Cupboard: Discursive Hauntings During the First Year of French Immersion Teaching in Canada (Lace Marie Brogden & Becky Page)10. From rats to language learners: The transition from the biochemistry laboratory to the language classroom in the first year of teaching (Y.L. Teresa Ting and Michael F. Watts)11. Formation of ESL Teacher Identity during the First Year: An Introspective Study (Alix Furness )12. My first year of language teaching in Japan (Yukie Iwamura)
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.016 | 0.014 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".