Fine-Tuning Climate Resilience in Marine Socio-Ecological Systems: The Need for Accurate Space-Time Representativeness to Identify Relevant Consequences and Responses
Bibliographic record
Abstract
Climate change triggers a wide mosaic of regional and local responses, often different to the large-scale variability in magnitude and direction. Because of the psychological connections (cognitive and emotional) with the frequency, intensity and age of a climatic event, people may have the capacity to recognize key variations at lower scales, especially those from which they perceive risk. Yet, the anticipatory actions and social engagement to respond or adapt to climate change are difficult to achieve, mostly when there exists a long psychological distance to climatic phenomena. Research about climate change communication provides clues about the relevance of place-based discussion to gauge risk perception and improve response protocols, their design and prioritization. It argues that strategies and actions required to face climate risks may widely differ depending on the scale and accuracy of the local representations displayed during discussions of climate impacts. This work examines how local attributes (from climate to social) operate and control place-specific risks and priorities, by comparing coastal communities in two locations, Cabo Pulmo, Mexico and Zanzibar, Tanzania, which are subject to different climate dynamics. This paper discusses the need to identify relevant climate risks/responses at the local level and how psycho-social factors (e.g., psychological distance, collective memory, and social engagement) may operate positively for building climate resilience. We also illustrate a workflow to increase and enhance collaboration between researchers and local people by promoting dialogue, participation and narratives that rigorously consider the local knowledge.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".