Transparent Government: Making Visible the Assemblages and Citizen Work
Bibliographic record
Abstract
The idea that a government who informs their citizens can better engage their citizens in democratic life underlies various laws and information practices. In the Ontario context, the sharing of the Hansard records of legislative debates online (from 1981 to present), the enactment of a freedom of information (FOI) law (1990), and the creation of eGovernment websites (mid 1990s onwards), each demonstrate initiatives centred around government's sharing of information with citizens. However, if we expect a change in “who can do what” (Florini, 2007, p. 1), citizens' information practices also require attention.L’idée qu’un gouvernement qui informe les citoyens permet une plus grande participation citoyenne à la vie démocratique est sous-jacente à de nombreuses lois et pratiques informationnelles. Dans le contexte de l’Ontario, le partage en ligne du journal des débats législatifs (de 1981 à aujourd’hui), la mise en œuvre de la loi sur l’accès à l’information (1990) et la création des sites web de gouvernance en ligne (de la mi 1990 à nos jours), sont autant d’initiatives centrées sur la notion du gouvernement qui partage de l’information avec ses citoyens. Cependant, si nous nous attendons à un changement du « qui fait quoi » (Florini, 2007, p. 1), les pratiques informationnelles de citoyens nécessitent également notre attention.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.068 |
| Scholarly communication | 0.021 | 0.031 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".