The Actor in 4 dimensions: A relevant methodology to analyze local environmental governance and inform Ostrom’s social-ecological systems framework
Bibliographic record
Abstract
This paper presents the Actor in 4 dimensions (A4D) model as a complementary tool to the Social-ecological systems framework (SESF) in order to better integrate individual and groups' representations into local environmental governance analysis. As the A4D is based on actors' representations of their social-ecological system (SES) and of its governance, it mainly informs the Actors subsystem of the SESF, even if it can also give useful insights for other framework's sub-systems. We define the SESF actor 's sub-tiers and the corresponding A4D indicators and highlight the complementarity between both approaches in order to operationalize the SESF. This parallel is exemplified by the case of Maio island (a small-scale fishing community in Cape Verde). Our comparison also highlights other assets of the A4D methodology for the advancement of environmental governance's study. •The A4D allows actors' participation and discussion on the SES and analyses common and divergent discourses and values between actors. •The A4D points to power relations by integrating strong, weak and absent actors in its analysis. •By highlighting subjective and reflexive elements, the A4D complements the SESF in their common attempt to analyze SES.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".