Intersections Between Government Data and <scp>AI</scp> Strategies: A Case Study of Technology Policies in Canada's Federal Service
Bibliographic record
Abstract
ABSTRACT AI and data are mutually influential, with AI outputs shaped by training data and data often generated, processed, and categorized by AI. The use of both AI and data by government organizations is guided by policy documents; existing research has explored data policies or AI policies but has rarely put both in conversation, despite their linked subject matter. We adopt a mixed‐methods approach to analyze the data and AI strategies of the Government of Canada, investigating whether the data‐AI relationship is reflected in policy documents. Our findings demonstrate a disconnect between Canadian data and AI policies, illustrate potential implications of this disconnect, and contribute to ASIS&T 2025 conversations about the necessity of information science for the responsible, ethical use of data and AI in government settings.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.060 | 0.026 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| 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".