Open Data and Official Language Regimes: An Examination of the Canadian Experience
Bibliographic record
Abstract
The open data moving is gathering steam globally, and it has the potential to transform relationships between citizens, the private sector and government. To date, little or no attention has been given to the particular challenge of realizing the benefits of open data within in an officially bi- or multi-lingual jurisdiction. Using the efforts and obligations of the Canadian federal government as a case study, the authors identify the challenges posed by developing and implementing an open data agenda within an officially bilingual state. Key concerns include (1) whether governments may use open data to outsource some information analysis and information services to an unregulated private sector through open data initiatives, thus directly or indirectly avoiding obligations to provide information analysis and information tools in official languages; and (2) whether the rush by governments to support the innovation agenda of open data may leave minority language communities both underserved and under-included in the development and use of open data.
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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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.020 |
| Science and technology studies | 0.076 | 0.032 |
| Scholarly communication | 0.020 | 0.007 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.004 | 0.009 |
| 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".