Government Information in Canada: Access and Stewardship
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.015 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".