Species at Risk Act: A Comprehensive Inventory of Legislative Documents, 1973-2017
Bibliographic record
Abstract
The long and complex history of the enactment of the Canadian Species at Risk Act (SARA) made this statute a prime target for a legislative and documentary history. The sheer volume of, and difficulty in locating, documents related to and considered in the development of SARA is vast. In a 30-year period, 18 bills relating to species protection were introduced in the House of Commons. In the past 15 years, 12 amending bills were introduced and 70 pieces of subordinate legislation were registered under SARA (largely regulations and Orders in Council). This legislative and documentary history includes all bills, amendments, and regulations, beginning with the 1973 Speech from the Throne and ending in February 2018. It also includes parliamentary papers and committee reports, related international treaties, regulatory process information, reports and backgrounders from various government departments and non-government organizations (NGO’s), and selected scholarly articles documenting the legislative process. The purpose of this legislative and documentary history is to facilitate an understanding of the legislative framework for SARA and assist with identifying primary legal documents related to endangered species research in Canada.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.023 | 0.034 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".