Growth, Transition, and Decline in Resource Based Socio-Ecological Systems
Bibliographic record
Abstract
Globalization transforms communities. Increased trade and technology can disrupt existing socio-ecological systems that may have persisted for hundreds or thousands of years. Whole socio-ecological systems may be destroyed or subsumed into a new dominant culture, as has occurred with many Indigenous cultures worldwide. In this context, I examine the Thule Inuit culture as a dynamic and multi-trophic socio-ecological system. Lessons from the study clarify fundamentals of trade and development: mutual benefits from trade rely upon equitable terms that sustain the original stewards of the ecological resource base; the ability to achieve such equitable terms is a function of governance mechanisms and capabilities; and the need for such institutional tools and governance mechanisms should be internally as well as externally recognized for all trading parties. The multi-trophic model includes three layers: a composite ecosystem resource base, a resource-dependent human population, and a top trophic human group of Traditional Ecological Knowledge (TEK) holders connected through caloric productivity and use. I calibrate the model with what can be known or deduced from the historical record and ecological evidence. I examine how new stressors to the Thule Inuit system, including the foreign commercial whaling and fur trading that brought particularly rapid shifts from the 1820s forward, transformed the system dynamics. Differences in the ways in which the two commercial enterprises evolved across Inuit communities, particularly in terms of net changes in access to calories and new technologies, provide comparative insights into how socio-ecological systems can gain or lose as the introduction of trade and technology can shift relative rates of return amongst ecosystem components.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".