Changing market culture in the <scp>P</scp>acific: Assembling a conceptual framework from diverse knowledge and experiences
Bibliographic record
Abstract
Abstract Addressing the multiple dimensions of gender inequality requires commitments by policy‐makers, practitioners and scholars to transformative practices. One challenge is to assemble a coherent conceptual framework from diverse knowledges and experiences. In this paper, we present a framework that emerged from our involvement in changing market culture in the Pacific, which we name a radical empowerment of women approach. We draw on detailed narratives from women market vendors and women‐led new initiatives in marketplaces to explain this approach. We argue that the primary focus of recently developed projects for marketplaces in the Pacific is technical and infrastructural, which is insufficient for addressing gendered political and economic causes of poor market management and oppressive conditions for women vendors. By exploring the complex array of motives and effects of the desire to transform or improve marketplaces in the Pacific, we caution against simplistic technical or infrastructural solutions. This paper also introduces the practice of working as a cooperative, hybrid research collaboration. The knowledges and analyses that we bring to this issue demonstrate that substantive analysis generated from diverse and shifting ‘locations’ and roles, but underpinned by a shared vision of, and commitment to, gender justice, can provide distinctive policy and research insights.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.043 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".