Accessing Democracy: The Critical Relationship between Academics and the Access to Information Act
Bibliographic record
Abstract
Parliament recognized the fundamental importance of protecting access to government information when it enacted the federal Access to Information Act. When the Act came into force on Canada Day 1983, Canada was just one of a handful of countries to have legislative protection of access to government information. Now, 27 years later, over 80 countries across the globe have enacted some form of access to information legislation. Although the world has followed Canada's lead in recognizing the importance of protecting access to government information, Canada has “fallen behind” (to borrow the descriptor used by journalist and author Stanley Tromp) and may even be “backsliding” (in the words of Laura Neuman of the Carter Center). What has gone wrong with the federal access regime? Why should legal studies scholars care? I address these questions in this article. I start by outlining the symbiotic role between academics and access to government information. I then identify three key factors that have contributed to the decline of the federal access regime: administrative resistance, legislative degeneration, and political indifference. Finally, I close by briefly discussing three ways in which scholars can continue to work to protect and promote access to information in Canada. Academics and Access Academics took the lead in advocating for access to government information in the 1960s and 1970s in Canada. One of the earliest advocates was Donald C. Rowat, a professor of Political Science at Carleton University. In a 1965 article entitled “How Much Administrative Secrecy?”, he summarized the key arguments in favour of protecting access to government information, writing Parliament and the public cannot hope to call the government to account without an adequate knowledge of what is going on; nor can they hope to participate in the decision-making process and contribute their talents to the formation of policy and legislation if that process is hidden from view.
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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 | 0.025 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.042 | 0.138 |
| Scholarly communication | 0.028 | 0.020 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".