Potentials and pitfalls of social capital ties to climate change adaptation: an exploratory study of Indigenous Peoples in the Philippines
Bibliographic record
Abstract
Climate change’s impacts vary across different geographical regions and societies, thus, underpinning the value of context-specific adaptation strategies grounded in local knowledge, social cohesion and community dynamics. This paper explores the potentials and pitfalls of social capital to climate change adaptation – an underexplored area of inquiry on climate change and Indigenous development literature. A qualitative case study design was used to conduct interviews and a focus group discussion with 14 Mamanwas and two government social workers in Eastern Visayas, the Philippines, from 2018 to mid-2019. Our findings suggest that while Mamanwas’ substantial bonding social capital ties contribute to their communal safety from weather extremes and adaptation to climate change, it has also unintentionally resulted in potentially adverse conditions, as can be deduced from their fragile bridging and linking capital ties with the broader community and government institutions. This research argues that social capital constitutes a vital social aspect of adaptation; therefore, policymakers and development workers must account for multiple scales and forms of adaptation, as well as acknowledge the importance of engaging, empowering and incorporating the political voice of Indigenous Peoples in crafting solutions on issues they consider relevant and urgent to their human development.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".