About Ecology: Structural Mechanisms to Improve Collaboration in Policy Issues
Bibliographic record
Abstract
The purpose of this chapter is to make citizens and society contributors to ecological responses of energy, climate, and policy issues in general at high levels of performance. It aims at reducing organisational hierarchies at the same time of increasing balanced relationships of organisational citizens with environmental customers in all kinds of natural contexts. It views organisations as non-trivial systems beyond input–output black boxes by studying structural mechanisms of people’s relationships within organisations to achieve collaborative cohesion and adaptation. Non-hierarchical relationships are designed to correct complexity imbalances between policymakers and actors and between customers and environmental suppliers. Operationally, these relationships aim at achieving shared trust, create truth and respect between each other. From the perspective of innovation, it offers an approach to achieve sufficient investment and adequate complexity management to decrease costs and enhance an organisation’s capacity to create and capture value for environmental and systemic transformations. Overall, as a contribution, it offers an approach for the design of non-trivial systems for people’s interactions, shaping these interactions towards people’s and group’s corrections of variety imbalances at all structural levels, from the most global to the most local. The chapter offers a management of complexity through an enterprise complexity model to improve policy, regulation, and implementation processes in organisations.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".