“French Teachers Can Figure It Out”: Understanding French as a Second Language (FSL) Teachers’ Work in the Context of the COVID-19 Pandemic
Bibliographic record
Abstract
In 2020, the coronavirus (COVID-19) pandemic forced teachers in Ontario to move online. Since then, teaching online or in hybrid models has been common across the province. To understand how French as a Second Language (FSL) teachers navigated these spaces, four Ontario French teachers were interviewed about their experience using educational technology and teaching online. Findings were analyzed in light of Hargreaves and Fullan’s (2020) reframing of classic understandings of teachers’ work in the context of the global pandemic. Findings show that factors influencing these teachers’ professional capital reflect common concerns among Canadian educators, alongside those specific to the FSL context. Participants' professional marginalization and seclusion demonstrates the importance of both the psychic rewards of teaching and cultures of collaboration. Ongoing efforts to capture ways in which teaching FSL has been shaped by the pandemic experience, therefore, require looking beyond individual classrooms to connected systems and systematic efforts of reform.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.030 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".