Towards Decolonizing French Language Education: Examples from Scotland (UK) and British Columbia (Canada)
Bibliographic record
Abstract
Abstract While many decolonization theories are rooted in issues related to the hegemony of the English language (e.g., Heller & McElhinny, 2017; Macedo, 2019), this chapter illuminates a lesser-known area of decolonization scholarship by considering the context of French Language Education across national settings (see also Bouamer & Bourdeau, 2022). This focus on French Language Education seems even more important as it takes place in the context of a struggle against the domination of Global English (e.g., Al-Hajebi, 2019; Červenková, 2020; Vallini, 2019), the rise of China and India as economic powers challenging the world order established in the wake of WW2 (e.g., Gazibo & Chantal, 2011), and the loss of influence of France on traditional French-speaking areas grouped within a geopolitical concept that has no real equivalence in English: La Francophonie.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".