Democratic governance in an age of datafication: Lessons from mapping government discourses and practices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it