MétaCan
Menu
Back to cohort
Record W3036769282 · doi:10.1177/1077801220923731

Operating-System Design and Its Implications for Victims of Family Violence: The Comparative Threat of Smart Phone Spyware for Android Versus iPhone Users

2020· article· en· W3036769282 on OpenAlexaff
Diarmaid Harkin, Ádám Molnár

Bibliographic record

VenueViolence Against Women · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsUniversity of Waterloo
FundersAustralian Communications Consumer Action Network
KeywordsAndroid (operating system)Computer securityInternet privacyPhoneMobile deviceSmart phoneEngineeringAdvertisingComputer scienceBusinessWorld Wide WebTelecommunicationsOperating system

Abstract

fetched live from OpenAlex

Spyware products sold to general consumer audiences are a greater threat to those who own Android devices than those who own iPhones. This is a consequence of the Android operating system being more permissive of software functionality, allowing third-party developers greater latitude to build programs of less-restrained capability. Such risks, however, are disproportionately carried by victims of family violence who are significantly threatened by the rise of spyware. This article reflects on the connections between coding choices and personal security risks, and the implications for responding to the use of spyware in the context of family violence.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0030.005
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.094
GPT teacher head0.335
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueViolence Against WomenSame topicPrivacy, Security, and Data ProtectionFrench-language works237,207