Save Us Before We Die: Unmasking Socioecological Systems Complexities and Their Implications On Coastal Fishers’ Livelihoods in Select Regions Of Yunlin, Taiwan
Bibliographic record
Abstract
This paper ranks among the initial empirical studies to explore the complex socioecological system (SES), dynamics, shifts, and their ramifications to coastal fisher communities in Taiwan. Participatory interactions with 38 respondents in Yunlin and ocean environmental data across Taiwan from 2010 to 2020 were utilized to capture SES vulnerability and resilience options for Yunlin, Taiwan. Findings revealed that Yunlin possesses valuable coastal resources that determine livelihood activities and SES functioning. The dominant fisheries resources have created unique livelihood identities, bonds, and SES networks among actors. SES and fishing-livelihood interactions are shaped along familial, community, and long-established ties. However, demographic shifts, for example, aging fisher and migrant youth populations, are altering SES interactions. With sea surface temperatures increasing by 1°C, bleak fishers’ livelihood futures are projected. This is worsened by massive ocean renewable energy projects, catapulting into declining livelihood benefits and coastal resource access. To mitigate these threats, diverse livelihood empowerment and SES resilience options are proposed. To expound these options, a co-designed sustainable coastal community system pathway with six critical resilience perspectives is developed. Enhancing SES and coastal communities’ resilience requires a holistic understanding of micro-level SES dynamics. Thus, coastal communities’ re-engagement and cross-sectional transdisciplinary research are needed. These could re-evaluate diverse spatial-temporal SES vulnerability dynamics and create better resilience perspectives for coastal fisheries and other livelihood sectors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".