Bibliographic record
Abstract
Most of the draft documents, memoranda, communications, and other textual materials amassed by government agencies do not become public record unless efforts are taken to obtain their release. One mechanism for doing so is “access to information” (ATI) or “freedom of information” (FOI) law. Individuals and organizations in Canada have a quasi-constitutional right to request information from federal, provincial, and municipal levels of government. A layer of bureaucracy has been created to handle these requests and manage the disclosure of information, with many organizations having special divisions, coordinators, and associated personnel for this purpose. The vast majority of public organizations are subject to the federal Access to Information Act (ATIA) or the provincial and municipal equivalents. We have been using ATI requests to get at spectrum of internal government texts. At one end of the spectrum, we are seeking what Gary Marx calls “dirty data” produced by policing, national security, and intelligence agencies. Dirty data represent “information which [are] kept secret and whose revelation would be discrediting or costly in terms of various types of sanctioning.” This material can take the form of the quintessential “smoking gun” document, or, more often, a seemingly innocuous trail of records that, upon analysis, can be illuminating. Dirty data are often kept from the public record. At the other end of the disclosure spectrum are those front-stage texts that represent “official discourse,” which are carefully crafted and released to the public according to government messaging campaigns.
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.
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 | 0.014 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.020 |
| Science and technology studies | 0.046 | 0.018 |
| Scholarly communication | 0.028 | 0.010 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.003 |
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".