Innovation for women, by women: A Case Study Exploration of Digital Social Innovation Projects by Female Innovators in Pakistan Afghanistan
Bibliographic record
Abstract
This thesis explores how female innovators in Pakistan and Afghanistan utilize digital social innovation (DSI) to empower and educate rural women through sharing knowledge on topics of women’s health. The study focuses on the innovators’ strategies for implementing DSI within capitalist-patriarchal societies and challenges colonial approaches to health education. Using narrative inquiry and digital archival research methods, this study engages with two female innovators, Pashtana Durrani and Saba Khalid, to understand how they navigate class distinctions and patriarchal systems while promoting knowledge sharing for rural women on topics of women’s health. The objective is to develop effective DSI tools and practices for empowering rural women and promoting sustainable feminist futures in Pakistan and Afghanistan, all while addressing systemic issues and contextualizing digital technology solutions. Key findings reveal both innovators’ adept use of DSI to bridge health education gaps. Although their community-rooted strategies challenge colonial norms and empower women through contextually relevant approaches, this empowerment is occurring within a capitalist-patriarchal framework.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".