Bibliographic record
Abstract
Photography has the ability to provoke ethical reflection and to provide an emotional connection to the reality of individual suffering (Hariman & Lucaites, 2016). Therefore, given the remarkable importance of visual communication in covering humanitarian crises, this short paper aims to problematize humanitarian photography practice and reflect on alternative ways of framing representations of refugee women’s life experiences outside mainstream media. Thus, I propose here an initial conversation regarding my doctoral research that focuses on self-representation of refugee women. I aim to investigate how self-representation can challenge the way to document refugee women’s life experiences by constructing through visual narration their identities and exiled memories. Therefore, the objective of this paper is to deromanticize the humanitarian discourse by reflecting on the photographer’s role in the field and by exploring alternative photography practices that frame nations affected by crises. The word crisis governs my work not only because refugee women are victims of a global refugee crisis resulting from armed conflict, natural disasters, and diseases, but also because of the daily subjective crises that these women face in lands that they now call home. Through self-representation, they can construct their stories beyond the problematic of conflicts. Thus, by reflecting on the activist potential of self-representation in framing of refugee memories it is possible to think of new opportunities to make their struggles visible in times of crisis.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".