The translation of ‘transparency’ in the Canadian press: an inquiry into symbolic power
Bibliographic record
Abstract
Despite new transparency regulation in 2002, a lack of transparency was identified only five years later among the causes of the financial crisis in North America. With this paradox in mind, the authors investigated the terms ‘transparency’ and ‘transparence’ in a corpus of seven Canadian newspapers (English and French) comprising nine million words, from the Dot-com crash of 2001 to the subprime crisis of 2007–2008. When contrasted with a test corpus of annual reports, the press corpus showed that during this period journalists mentioned ‘transparency’ intermittently, and most frequently in 2007–2008, whereas the banks used it with a steady increase. When represented on a Transparency Perception Continuum, the data showed the press as critically pointing to a lack of transparency, and the banks as positively or neutrally discussing transparency. It was also evidenced that English-Canadian reporters used a wider array of sources than did their French-speaking counterparts when recasting statements on transparency. The francophone press seldom quoted American sources, selecting instead statements originally made in French by local banks in the province of Québec. The findings show that by avoiding translation the French-Canadian press contributed to a more bank-centric view on transparency, entangled in the production of a dominant discourse.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.021 | 0.031 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".