Democratic governance in an age of datafication: Lessons from mapping government discourses and practices
Bibliographic record
Abstract
There is an abundance of enthusiasm and optimism about how governments at all levels can make use of big data, algorithms and artificial intelligence. There is also growing concern about the risks that come with these new systems. This article makes the case for greater government transparency and accountability about uses of big data through a Government of Canada qualitative research case study. Adapting a method from critical cartographers, I employ counter-mapping to map government big data practices and internal discussions of risk and challenge. I do so by drawing on interviews and freedom of information requests. The analysis reveals that there are more concerns and risks than often publicly discussed and that there are significant areas of silence that need greater attention. The article underlines the need for our democratic systems to respond to our new datafied contexts by ensuring that our institutions make changes to better protect citizen rights, uphold democratic principles and ensure means for citizen intervention.
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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 | 0.047 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.037 | 0.106 |
| Scholarly communication | 0.028 | 0.031 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.004 | 0.008 |
| 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".