Mobile Phones & Literacy: Empowerment in Women's Hands: a Cross-case Analysis of Nine Experiences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it