Mattering Matters: Agency, Empowerment, and Mobile Phone Use by Female Microentrepreneurs
Bibliographic record
Abstract
This article attempts to enrich our understanding of the role that mobile phones play in the empowerment of women in the developing world. We adapt and explicate an innovative social psychological concept, “mattering,” embed it in the literature that examines the impact of mobile phones on social development outcomes, and consider the utility of mattering for the ICT4D community. Mattering is the perception that others are aware of, interested in, and depend on us. Based on a sample of 335 female microentrepreneurs in Chennai, India, we created a valid and reliable measure of mattering and its three dimensions. Mattering was predicted by (1) entrepreneurial expectations, an element of an individual's mindset; (2) social use of mobile phones; and (3) the perceived benefits of mobile phones for maintaining business networks. Findings suggest that mobile phone use plays a significant role in contributing to female entrepreneurs' perception that they matter.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".