Points of Departure: Developing the Knowledge Base of ESL and FSL Teachers for K-12 Programs in Canada.
Bibliographic record
Abstract
In this paper, I examine the contextual factors that impact the development of knowledge base of teachers of English as a second language (ESL) and French as a second language (FSL) for teaching in Kindergarten through Grade 12 programs in Ontario. Using a sociocultural orientation to second language teacher education and prominent knowledge base frameworks from the field, I discuss how a variety of local contextual factors impact the development of teacher candidates’ (TC) knowledge base in pre-service teacher education programs in Canada. Individual factors include: the linguistic and cultural backgrounds of candidates’ in ESL and FSL programs, the TCs’ language proficiency in the target language, their personal experiences and understanding of language development, and their familiarity with real life experiences of ESL and FSL students. Beyond their own experiences, integral to TCs knowledge base are the range of student populations they could serve and the variety of language teaching contexts they can encounter in the Ontario context. I discuss the implications of such nuances for policy and practice in language teacher education programs across Canada.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".