A Proposed Practical Problem-Solving Framework for Multi-Stakeholder Initiatives in Socio-Ecological Systems Based on a Model of the Human Cognitive Problem-Solving Process
Bibliographic record
Abstract
A practical problem-solving framework is proposed for multi-stakeholder initiative (MSI) problem-solving processes involving socio-ecological systems (SES), so-called wicked problems, based on insights borrowed from a model of the individual human, cognitive problem-solving process. The disciplined facilitation of the multi-stakeholder process, adhering to the steps recognized in the individual process, is meant to reduce confusion and conflict. Obtaining a one- to three-sentence human-language description of the desired system state, as a first step, is proposed in multi-stakeholder initiatives for reasons of goal congruence and trust building. The systematic, stakeholder-driven subdivision of obstacles into larger numbers of simpler obstacles is proposed in order to obtain a list of "what needs to be done," inviting a more rational and goal-driven conversation with resource providers. Finally, obtaining and maintaining stakeholder buy-in over the course of the problem-solving effort is reinforced by reflecting back to all stakeholders, as a communication device, a dynamic, visual problem-solving model, taking into account the diversity of cognitive and individual capacities within the stakeholder group in its presentation. Mathematical parameters for gauging applicability of the proposed framework 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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".