To count or not to count ‘francophones.’ Reading language planning and policy through the words of quantification
Bibliographic record
Abstract
This article uses the concepts of ‘language ideologies’ and ‘governmentality’ to examine how language quantification processes tightly intertwine with language policy and planning (LPP) issues. Comparing four ways of (not) counting ‘francophones’ across the contexts of France, Switzerland, Canada, and the International Organization of La Francophonie, this article focuses on words rather than numbers. It unpacks how scientific and political discourses intertwine across different ways of defining ‘francophones,’ finding data to count them, and producing knowledge on ‘francophones’ for converging or diverging LPP purposes involving French language. The analysis shows the potential of reading into the scientific and political discourses that make those statistics (im)possible, in order to understand what the variations in these processes tell us about unequal power relations among speakers in the politicized management of a language, its variations, and its speakers.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".