“I am a farmer”: Young women address conservation using photovoice around Tiwai Island, Sierra Leone
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".