An even less convenient truth : addressing the challenge of sustainable development through an integration of cognition and culture
Bibliographic record
Abstract
‘Sustainable development,’ or how to achieve durably desirable states in our planet’s nested social-ecological systems, has been heralded by many as the core civilizational challenge of the 21st century. Adding to this challenge is the fact that the scientific study of how to model and manage such complex systems is confounded by a number of archaic intellectual legacies from predecessor disciplines. Chief among these is a relatively crude, low-resolution ‘rational actor’ theory of human behaviour, which lies in tension with a range of more recent, empirical insights regarding how humans absorb information, make decisions, and act, in situ. I argue that, while authors widely acknowledge the former theory to be insufficient, terminological inconsistencies and conceptual opacity have prevented the latter insights from being fully integrated into much sustainable development research. This dissertation aims to help bridge that gap on the level of both theory and practice. First, I present an accessible, original synthesis of cumulative recent findings on human cognition. This synthesis suggests a key object of analysis should be the particular ways in which people reduce the deep complexity of their social-ecological context into actionable information. I then apply this theoretical lens to the study of two areas designated by the UN as sites for experimentation with the concept of sustainable development: Mt. Carmel UNESCO Biosphere Reserve in Israel, and Clayoquot Sound UNESCO Biosphere Reserve in British Columbia, Canada. Both Mt. Carmel and Clayoquot Sound are reeling from major ecological shifts, and discordant multistakeholder relations. In my data chapters, I show that by (a) applying my synthesized theoretical lens to an analysis of how the various stakeholders perceive their local context, and (b) adapting and combining a range of elicitation and analysis methods that heretofore have been applied in isolation, I am able to generate insights that have direct, actionable significance for the management of these sensitive, politically fraught social-ecological systems. I conclude with a discussion of implications, caveats, prospects of scalability, and suggestions for future research.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.067 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| 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".