Catching sea cucumber fever in coastal communities: Conceptualizing the impacts of shocks versus trends on social-ecological systems
Bibliographic record
Abstract
Research on vulnerability and adaptation in social-ecological systems (SES) has largely centered on climate change and associated biophysical stressors. Key implications of this are twofold. First, there has been limited engagement with the impacts of social drivers of change on communities and linked SES. Second, the focus on climate effects often assumes slower drivers of change and fails to differentiate the implications of change occurring at different timescales. This has resulted in a body of SES scholarship that is under-theorized in terms of how communities experience and respond to fast versus slow change. Yet, social and economic processes at global scales increasingly emerge as ‘shocks’ for local systems, driving rapid and often surprising forms of change distinct from and yet interacting with the impacts of slow, ongoing ‘trends’. This research seeks to understand the nature and impacts of social shocks as opposed to or in concert with trends through the lens of a qualitative case study of a coastal community in Mexico, where demand from international seafood markets has spurred rapid development of a sea cucumber fishery. Specifically, we examined what different social-ecological changes are being experienced by the community, how the impacts of the sea cucumber fishery are distinct from and interacting with slower ongoing trends and how these processes are affecting system vulnerability, adaptations and adaptive capacity. We begin by proposing a novel framework for conceptualizing impacts on social systems, as comprised of structures, functions, and feedbacks. Our results illustrate how the rapid-onset of this fishery has driven dramatic changes in the community. New challenges such as the ‘gold-rush-style’ arrival of new actors, money, and livelihoods, the rapid over-exploitation of fish stocks, and increases in poaching and armed violence have emerged, exacerbating pressures from ongoing trends in immigration, overfishing and tourism development. We argue that there is a need to better understand and differentiate the social and ecological implications of shocks, which present novel challenges for the vulnerability and adaptive capacity of communities and the sustainability of marine ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".