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Record W6959072357 · doi:10.1080/10130950.2009.9676224

“I am a farmer”: Young women address conservation using photovoice around Tiwai Island, Sierra Leone

2009· article· en· W6959072357 on OpenAlexaff

Bibliographic record

VenueAgenda · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsMcGill UniversityQueen's University
Fundersnot available
KeywordsPhotovoiceSierra leoneParticipatory action researchCitizen journalismFocus groupIdentity (music)Participant observationYouth studiesConstruct (python library)

Abstract

fetched live from OpenAlex

You can express a different story through art. What happens when you use arts-based inquiry to explore environmental knowledge? How can artistic expression value and construct alternative knowledges that might otherwise be overlooked or silenced? Holistic and justice-oriented trends in environmental education argue for the integration of social identity within ecological issues. A participatory community-based photovoice project investigated environmental, community and cultural assets to be protected for future generations in the rural communities around Tiwai Island in Sierra Leone. An intergenerational project with youth and elders, men and women, the participant communities identified 10 themes for conservation and development through their photographs: Food and Agriculture, Education, Religion, The Barray, Water, The House, Transportation, Toilets, and The Forest. However, in such a project the unique expressions of young women's voices may be overlooked. What are young women saying? By creating space to focus specifically on the young women in the study, I hoped to address this challenge of mainstreaming gender in community-based work. The performance piece that emerged elaborates, nuances, and particularises how Food and Agriculture are the most important features of place for young women living in the communities around Tiwai Island.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.697
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.503
GPT teacher head0.574
Teacher spread0.070 · 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 designNot applicable
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

Citations3
Published2009
Admission routes1
Has abstractyes

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