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Record W2227759199 · doi:10.1080/02732173.2015.1086287

The experiential gap in disaster research: Feminist epistemology and the contribution of local affected researchers

2015· article· en· W2227759199 on OpenAlex
Kristen Barber, Timothy J. Haney

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSociological Spectrum · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsMount Royal University
FundersDivision of Social and Economic Sciences
KeywordsExperiential learningSociologyExperiential knowledgeEpistemologyPsychologyPedagogyPhilosophy

Abstract

fetched live from OpenAlex

In this article, we make the case for a situated knowledge of disasters. By applying a feminist standpoint framework, we argue that an ethic of “objectivity” and a privileging of the unattached researcher creates an experiential gap in the disaster literature whereby researchers who themselves experience disaster are undervalued and underrepresented. We analyze reflexive accounts by disaster researchers to show what epi stemological barriers emerge from conventional processes of inquiry and the systematic disadvantaging of local, affected researchers. We also study patterns in articles by “outsider” and “insider” researchers, focusing on differences and similarities in research questions, reflexivity, relationships with and access to participants, and larger theoretical goals. This comparison reveals that the unique position of affected researchers can help to bridge formal knowledge and practical life knowledge, creating new and worthwhile paths to understanding the social effects of disaster.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.016
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.168
GPT teacher head0.422
Teacher spread0.255 · 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