Communication for social change: the importance of NGO–community collaboration in supporting social transformation
Bibliographic record
Abstract
Despite a growth in scholarship on feminist and gender advocacy in Ghana, little attention has been paid to how non-governmental organizations (NGOs) have leveraged digital platforms to communicate. Using African technocultural feminist theory (ATFT), we analyse NGOs’ digital communications, paying attention to how they use these platforms to define their organizational identities while challenging gender stereotypes. We argue that although NGOs use digital platforms to communicate, their praxis may not necessarily be accessible to the communities with which they work; these platforms enable them to share their women’s empowerment programmes with other stakeholders while bringing awareness to issues affecting marginalized people in these communities. This study presents practical strategies for effectively communicating gender advocacy in the Ghanaian context and beyond.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.024 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.001 | 0.020 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".