“It seems that climate change is already harming us all”: Complex climate change, health, and socio-ecological risks for Mexican fishing communities
Bibliographic record
Abstract
Climate change impacts on the ocean increasingly challenge coastal communities’ livelihoods, food security, cultural heritage, health, and well-being. While these impacts are unfolding in real time for small-scale fisheries (SSF) in the Global South, research examining the human health dimensions of these climate-induced disruptions is nascent. Therefore, we documented diverse experiences of climate change impacts on marine ecosystems, characterized climate-socio-ecological factors that shape health and wellbeing, and identified barriers and enablers of health-related climate change adaptation in Mexican fishing communities. Drawing on a community-based approach and prioritizing the voices of fishing communities, we partnered with two fishing cooperatives on the Pacific coast of Baja California. We employed an integrative qualitative methodology, combining daily-routine accompaniment methods, open-ended interviews, and photo-elicitation techniques to capture community members' perspectives, emotions, and local knowledge (n = 54). Data were analysed using reflexive thematic analysis and are presented through detailed narratives. Fishers explained how environmental changes impacted local livelihoods, intergenerational knowledge systems, and challenged cultural identities and ways of life. Community members described the importance of ecosystem interactions and public health, including how the ocean underpins mental and physical health, and overall well-being. Finally, fishers noted different adaptation barriers, including local to national challenges such as infrastructure, regulations, and gender roles, as well as international pressures. They also described adaptation enablers, including ecosystem-based and social-based strategies, particularly related to conservation efforts. This study highlights how fishing communities’ experiences and knowledge, enduring cultural narratives, and collective sustainability efforts shape local climate change responses and resilience, supporting health and well-being. • Climate change is influencing Mexican fishing community's health and well-being. • Social cohesion and cultural identity play crucial roles in the climate-health nexus. • Socio-ecological health systems are dynamic and influenced by climate change. • Climate adaptation barriers include infrastructure, regulations, and gender equity. • Local leadership and sustainability initiatives are key adaptation enablers.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".