Fine-tuning Failure: How to Fail to Succeed
Bibliographic record
Abstract
The Gulf and Caribbean region, like many other parts of the world, is littered with failed or failing fisheries projects.They come in all sorts, shapes, and sizes.Under the rubric of fisheries governance, with its emphasis on civil society participation as an expected factor of success, of particular interest are projects that involve state fisheries authorities or fisherfolk non-governmental organizations and external donor agencies.Such project partnerships, if well designed, are intended to yield win-win outcomes.In this paper I examine the proposition that perhaps these projects are short term win-win, even if they truly fail in the long term.Such 'beneficial' failure may be the result of collusion amongst the actors that has serious implications for governance.Project failure (in its literal sense) is easier to define than success.The key is to refer to agreed goals and objectives.Goal displacement, adaptation or other adjustments must be taken into account.Still, observations suggest that it is not uncommon for grantees to 'almost succeed' on a recurring basis that provides the grantors with opportunities to continue funding the same grantees, and to forecast the likely outcomes.This fine-tuning of failure perpetuates a mutually beneficial supply and demand until some perturbation breaks the cycle.Fine-tuning failure is a coping strategy that constrains self-organization, adaptive capacity and resilience in fisheries governance.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.038 | 0.019 |
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".