Bibliographic record
Abstract
Canada has the world’s longest coastline and also contains more fresh water than any other nation. As a nation of coasts, however, the record of action to protect and manage these coastlines is limited. While many other nations have acted to create comprehensive and integrated coastal zone management regimes, Canada has not. Instead, the Canadian response has been more of a patchwork of activities, respecting the complex jurisdictional framework of a multi-tiered government organization, and the challenge of response to very different issues in different parts of the nation. The following report is a snapshot in time of coastal zone management in Canada, and was completed in 1996 as part of background material to support the new Oceans Act. It is an historical view: the rapidly evolving jurisdictional changes in the face of federal and provincial program reviews and resulting cutbacks at both levels are resulting in rapid change. This change is both reducing the range of information available on coastal resources, and at the same time helping to forge new partnerships between all the partners who use the coastal zone- in order to deal effectively with common issues. While the fundamental principles of integrated coastal zone management remain unchanged- the need for integrated multi-partner actions based on good information- the emphasis on “bottom line ” thinking, and the rapid loss of corporate memory as employee early departure/incentive programs take full effect and major programs (e.g.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.615 | 0.359 |
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".