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Record W4382536903 · doi:10.1080/0966369x.2023.2228502

Seeing empathy as resistance: a conjunctural photovoice study of women and mining in Indonesia

2023· article· en· W4382536903 on OpenAlexaff
Tracy Glynn, Siti Maimunah

Bibliographic record

VenueGender Place & Culture · 2023
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsSt. Thomas University
Fundersnot available
KeywordsSolidarityPhotovoiceEmpathyResistance (ecology)SociologyEthnographyIndividualismStorytellingGender studiesPolitical sciencePublic relationsPsychologySocial psychologyLawVisual artsAnthropologyNarrativeArt

Abstract

fetched live from OpenAlex

Mining is increasingly under the scholarly microscope for its social and environmental impacts, including its uneven gender impacts. To study resistance of women across two communities affected by a long-established nickel mining and smelting operation in Indonesia, we paired photovoice with a discussion on conjunctures in the photo-stories. Photovoice is a visual ethnographic method that combines photography and storytelling to explore answers to research questions centered on how research participants make sense of their social worlds. By adding a discussion of conjunctures found in the photo-stories, we noted the combined methods facilitated empathetic responses and cross-community solidarity, a powerful antidote to the hyper-individualism and social discord fostered by mining interests in the neoliberal capitalist period. As Indonesia plans to open dozens more nickel mines and smelters, like the one in our study, in the rush to supply nickel for electric vehicle batteries, our study challenges scholars to look for empathy and solidarity. Seeing and exercising empathy and solidarity are important as extractive interests are expected to continue divide and conquer tactics to secure land and resources for exploitation.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.081
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.014
GPT teacher head0.228
Teacher spread0.214 · 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

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations7
Published2023
Admission routes1
Has abstractyes

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