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Record W2990611346 · doi:10.1007/s10113-019-01562-z

Exploring methodological approaches to assess climate change vulnerability and adaptation: reflections from using life history approaches

2019· article· en· W2990611346 on OpenAlexfundno aff
Chandni Singh, Mark Tebboth, Dian Spear, Prince Alvin Kwabena Ansah, Adelina Mensah

Bibliographic record

VenueRegional Environmental Change · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change, Adaptation, Migration
Canadian institutionsnot available
FundersDepartment for International DevelopmentInternational Development Research Centre
KeywordsLivelihoodVulnerability (computing)NeglectAdaptive capacityValue (mathematics)Adaptation (eye)SociologyClimate changeSocial psychologyEnvironmental resource managementPositive economicsPsychologyGeographyEconomicsAgricultureComputer science

Abstract

fetched live from OpenAlex

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.

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

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 armCategoriesStudy designConfidence
gptno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Qualitativehigh
opusMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Qualitativemedium
models splitAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.098
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0980.064
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0150.062
Scholarly communication0.0120.016
Open science0.0050.016
Research integrity0.0040.008
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.966
GPT teacher head0.414
Teacher spread0.552 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designQualitative
DomainMethods
GenreMethods

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".

Quick stats

Citations34
Published2019
Admission routes1
Has abstractyes

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