Bibliographic record
Abstract
Canada is a Federal State with a Federal Government, ten Provincial and three Territorial Governments. There is a constitutional division of legislative authorities and a resulting division of policy authorities. Although not a complete list, the Federal government administers trade and commerce, taxation, criminal law, public debt, fisheries, currency and coinage, banks and banking, and First Nations (i.e. indigenous peoples) and First Nations’ lands. The Provinces administer, among other issues, the sale of non-Federal lands, hospitals, municipal institutions, local works and undertakings and matters of a local or private nature in the Province. Jurisdiction in some areas, such as environment, is shared. The Federal Government may utilize a number of policy development tools such as: legislation and regulations (e.g. Canadian Environmental Assessment Act), guidelines and codes of practice (e.g. Greening Government Operations), funding programs (e.g. National Infrastructure Program) and management plans (e.g. Parks Management Plans). Developing policy options includes, where necessary, Strategic Environmental Assessment (SEA). In theory, Canada has been committed to assessing the potential environmental implications of Federal policies since 1984, the Environmental Assessment Review Process Guideline Order defined a “proposal” to include “any initiative, undertaking or activity for which the Government of Canada has a decision making responsibility” (Noble 2002). However, SEA as we know it began in Canada in 1990 when the Federal Government’s Cabinet of Departmental Ministers directed their respective Departments to consider environmental concerns at the strategic level of decision-making. This Cabinet Directive was revised in 1999 to strengthen the role of SEA by clarifying obligations of Departments and Agencies and linking environmental assessment to the implementation of sustainable development strategies.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.088 | 0.012 |
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".