“There’s only one pot of money it can come from”: A Corpus-Based Analysis of the International Baccalaureate in Canada’s Provinces
Bibliographic record
Abstract
This study has a dual purpose: (1) to show how computer-assisted discourse analysis of a 1.5-million-word specialized corpus can uncover patterns of language use that provide insights into the beliefs and values of a particular social group, making possible a “new way of looking at old puzzles” (Stubbs, 2010); and (2) to examine how the International Baccalaureate (IB) is represented in the Canadian provincial context. Although keywords reveal lexical differences in how the IB is represented in each province, in-depth contextual analysis indicates a similarity of concerns, particularly with regard to funding and cost of IB programs. Keywords: International Baccalaureate, corpus linguistics, discourse analysis, keyword analysis, education funding, policy, media
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".