Visual Storytelling and Socioenvironmental Change: Images, Photographic Encounters, and Knowledge Construction in Resource Frontiers
Bibliographic record
Abstract
Practices of visually representing places of resource extraction and land degradation can be deeply contentious, embedded in a wide variety of values, ethics, goals, and relations. Photographs are pervasively used to generate narratives about environmental change, particular social groups, and places. Yet, the sociocultural processes and power relations at play in producing “visual knowledge” and interpreting images often remain underexplored, with limited attention to how photographs and visual storytelling are engaged to (re)orient discussions about change. Challenging ways of seeing, this article discusses relational practices around photography and the narrating, experiencing, and circulating of images. It explores experiences with photovoice—a methodology aimed at realigning the dynamics of who decides what photos matter, how, why, and with what implications, sometimes pitched as a way to “decolonize” research. The study examines interactions in a village in Central Kalimantan, Indonesia, where women shared visual stories to express challenges they face in relation to deforestation and other landscape changes, depleted gold deposits, limited livelihood options, and other themes, conveying place histories and ideas about home, identity, governance, and community. Reflecting on intergenerational dialogues and anxieties about the future, the analysis considers photovoice processes in refracting everyday struggles, arguing for feminist epistemologies that carefully attend to the situated ethics and contingent performative powers of visual storytelling where multiple forms of resource extraction powerfully shape community life. The article calls for greater focus on women’s place-based storytelling and its communicative power, highlighting the significance of positionality when studying socioecological visualization, affect, and change. Key Words: feminist visualization, Indonesia, participatory visual methods, photovoice, resource extraction.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".