Mobile Phones & Literacy: Empowerment in Women's Hands: a Cross-case Analysis of Nine Experiences
Bibliographic record
Abstract
This is the ninth book from UNESCO Publishing that has been reviewed for Science and Public Policy since 1987, an average of one to two per decade, and we are honoured to provide the first one since 2005. Mobile Phones & Literacy: Empowerment in Women’s Hands is a well-written report, and worthy of a review. In this review, we examine the complexity of the topic with a focus on the elements highlighted in the title: literacy, empowerment, mobile phones, and gender. The first few pages enforce that this report is a call to strengthen people-centred and inclusive Information Societies. We as reviewers, and no doubt many readers of Science and Public Policy, support the goal of equal access and critical engagement through information sharing. Given this, it is a delight to be provided on-the-ground examples of where such work is being applied experientially. The report presents a cross-case analysis of nine mobile phone initiatives. One reviewer especially enjoyed reading Annex 1: ‘Projects reviewed’, which provided a better understanding of the studies outside of the context of the book. It is important to note that the programs chosen were implemented in countries with low and median values on the United Nations Development Programme and the Human Development Index, as well as with higher gender inequality, according to the UNDP’s Gender Inequality Index. As such, it was informative to see a level of transparency in listing the numerous challenges that come with using mobile phones for literacy and empowerment; many discussions tend to skip over the issues.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".