Balance: A Critical Principle in the Design of a Decentralized Decision Process
Bibliographic record
Abstract
"A proposal for stakeholder involvement in fisheries management and decision making as the central strategy for fisheries management in Saskatchewan in the 1990s underwent public review between September 1990 and June 1991. The strategy was presented in broad, general terms, without proposing specific implementation details. Seven principles were suggested as a framework for local co-management structures and initiatives. Positive public response was strong, however some concern was raised about the lack of specific detail. This paper presents a brief case study of the recent Saskatchewan experience with public consultation regarding the establishment of a cooperative, decentralized decision process. Initial government rationale is presented, and public response discussed. The relationship between stability and equity provided by a central framework and flexibility and responsiveness of a cooperative localized process is considered. The paper concludes with a description of the actions proposed by the authors to find the balance required for a decision process which combines the stability and equity of a central framework with the flexibility and responsiveness of decentralization."
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 | 0.102 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.049 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".