Bibliographic record
Abstract
Public access to government information forms the foundation of a healthy liberal democracy. Because this information can be precarious, it needs stewardship. Government Information in Canada provides analysis about the state of Canadian government information publishing. Experts from across the country draw on decades of experience to offer a broad, well-founded survey of history, procedures, and emerging issues—particularly the challenges faced by practitioners during the transition of government information from print to digital access. This is an indispensable book for librarians, archivists, researchers, journalists, and everyone who uses government information and wants to know more about its publication, circulation, and retention. Contributors: Graeme Campbell, Talia Chung, Sandra Craig, Peter Ellinger, Darlene Fichter, Michelle Lake, Sam-chin Li, Steve Marks, Maureen Martyn, Catherine McGoveran, Martha Murphy, Dani J. Pahulje, Susan Paterson , Carol Perry, Caron Rollins, Gregory Salmers, Tom J. Smyth, Brian Tobin, Amanda Wakaruk, Nicholas Worby
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".