Picture this! Vulnerable women’s perspectives on SDGs prioritisation
Bibliographic record
Abstract
This study examines the most significant development risk women at risk of being left behind in the SDGs implementation experience. It uses the photovoice method and the social amplification of risk framework (SARF) to highlight development risks in participants’ everyday lives that increase their likelihood of being left behind. The findings demonstrate that while the challenges faced by at-risk individuals can be complex, frameworks such as the SARF can assist in understanding the underlying socio-cultural processes that intensify the effects of risks faced by those vulnerable to being left behind. The priorities identified by participants suggest that aside from targeting the needs of the farthest behind, initiatives prioritised in SDGs localisation should also harness the linkages between the SDGs to optimise the limited time and resources available for SDG implementation. The findings are relevant to identifying strategies to operationalise the ‘leave no one behind’ (LNOB) commitment effectively and efficiently in developing contexts. This article was published open access under a CC BY licence: https://creativecommons.org/licences/by/4.0 .
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".