Bibliographic record
Abstract
African women’s emerging visibility as social and political actors has received a lot of attention in the past two decades. Scholars have explored women’s political movements and sociocultural activism from various perspectives to expose their contributions to social change. Although this scholarship has expanded to incorporate multiple voices as well as expose the contemporary strategies of resistance women engage in to overcome difficult challenges, there seems to be little research on ordinary women as they also confront their daily challenges in hope of improving their situations. This research takes up this gap by exploring a Ghanaian woman’s resistance in the face of medical adversity and the outcomes that emerged. I employ the concept of metis, taking definitions from scholars such as Flynn et al., Detienne and Vernant, Dolmage, and Hawhee, to examine how the woman negotiated her situation to bring it to the attention of health authorities and the general public. I use rhetorical analysis to interpret, analyze, and evaluate the rhetorical actions that took place in response to the issue. Through this research, it becomes evident that metis plays an important role in allowing us to better understand the ways women, especially the marginalized, resist oppression. The study also broadens our knowledge of African women’s strategies of resistance to include metistic strategies that the vulnerable employs to effectively negotiate adverse circumstances.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.026 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".