Mobile phones, gender‐based violence, and distrust in state services: Case studies from Solomon Islands and Papua New Guinea
Bibliographic record
Abstract
This paper examines the potential benefits and pitfalls of mobile phones for accessing social services, particularly in response to gender‐based violence, in Solomon Islands and Papua New Guinea. Drawing on 13 months of ethnographic field research, I show how mobile phones increase rather than decrease perceived distances between social service providers and those they intend to serve. Mobile phones exaggerate the visibility of the shortcomings of the Solomon Islands and Papua New Guinea states and solidify an already entrenched distrust in the state and state services. This distrust is accentuated in experiences with mobile phone‐based mediations of gender‐based violence. Despite the positive influences of mobile phones, they are also recognised as conduits of violence. As such, mobile phones are not only morally ambivalent technologies but also, at times, actively disliked and their use discouraged. This challenges the optimism that surrounds many information and communication technologies for development (ICT4D) projects. When assessing the potentials, successes, and failures of ICT4D programmes, there is a need to pay more attention to the consequences of ‘negative’ or ‘unreliable’ usages of mobile phones as relational technologies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".