Understanding the approaches for accommodating multiple stakeholders' interests
Bibliographic record
Abstract
Conflict and collaboration are often treated as mutually exclusive modes of stakeholder interaction, with little understanding of the contexts in which stakeholder relationships take place. The conceptual framework in this paper addresses accommodating multiple interests as an evolving, cyclical, iterative process, swinging back and forth from collaborative to conflictive situations. A typology is presented with nine contextual facets that come into play in accommodating multiple interests the nature of the problem, the stakeholders, the convenor, the networks, stakeholders' capacities, stakeholders' choices over procedures to deal with conflict, negotiation, and dispute resolution. The nine facets function as lenses through which to analyse multiple stakeholder situations. The typology is used to analyse four existing approaches, Collaborative Management, Collaborative Learning, Rapid Appraisal of Agricultural Knowledge Systems (RAAKS) and "linked local learning". A set of criteria to assess their impact is developed, and desirable future directions for methodological development are discussed.
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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 | 0.032 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.019 | 0.033 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".