Current Approaches to Co-Management in Manitoba
Bibliographic record
Abstract
"Cooperative management, as a regime for sharing resource management authority between government agencies, interest groups and user communities has been introduced in Manitoba on a number of occasions and in a variety of settings. It is important to note at the outset that co-management does not have a single prescription: it can denote stronger forms of community involvement (i.e. formal joint management of resources, or even self-management of resources by the communities themselves), or it can mean weaker forms of local involvement (i.e. consultative management through an advisory board). This study explores the current status of co-management in Manitoba, with an emphasis on the level of community involvement in such management strategies. While the term "co-management" tends to be used primarily in the area of wildlife and fisheries management, the following examples demonstrate that co-management can also apply to other resource-based industries, such as forestry and wild rice harvesting. The rationale for such agreements, and the issues or problems particular to each setting are explored."
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".