Exploring methodological approaches to assess climate change vulnerability and adaptation: reflections from using life history approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract People in developing countries face multiple risks, and their response decisions sit at the complex and often opaque interface of climatic stressors, constrained resource access, and changing livelihoods, social structures, and personal aspirations. Many risk management studies use a well-established toolkit of methodologies—household surveys, focus group discussions, and semi-structured interviews. We argue that such methodological conservatism tends to neglect the dynamic and differentiated nature of livelihood decisions. Since different methodologies privilege different portrayals of risk and response, we highlight how plural methodological approaches can capture a broader range of perspectives and problematisations. In this paper, we draw on life history (LH) interviews across four countries (Kenya, Namibia, Ghana, and India) to offer one way of expanding current methodological approaches on vulnerability and adaptation. We argue that LHs offer four key ‘value additions’. First, LHs give insights into the multiple and interacting nature of drivers of response behaviour. Second, they highlight intra-household dynamics to demonstrate how people with differential power shape risk management decisions. Third, LHs support explorations of past decisions, present situations, and future aspirations, thus producing temporally nuanced enquiries. Fourth, they provide a powerful analytical lens to capture the interplay of motivations, aspirations, and values on livelihood choices and adaptation outcomes. By adding value in these four ways, LHs challenge assumptions about how and why people respond to multiple risks and offer a nuanced understanding of adaptation processes.
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Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| opus | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it