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Record W2985009384 · doi:10.1145/3359275

Of Ulti, 'hajano', and "Matachetar otanetak datam"

2019· article· en· W2985009384 on OpenAlexafffund
S M Taiabul Haque, Pratyasha Saha, Muhammad Sajidur Rahman, Syed Ishtiaque Ahmed

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2019
Typearticle
Languageen
FieldComputer Science
TopicICT in Developing Communities
Canadian institutionsUniversity of Toronto
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsComputer-supported cooperative workSituatedVernacularSociologyConfidentialityInternet privacyEthnographyThe artsComputer scienceWork (physics)Political scienceComputer securityEngineeringLawArtificial intelligenceLinguisticsAnthropology

Abstract

fetched live from OpenAlex

Strategies of hiding information over communication media have long been an interest of CSCW and related communities. Most of the studies in this area have focused on various computational means of data protection and their vulnerabilities, and occasionally on social practices situated in the West. However, communities in the Global South often have a rich trove of vernacular arts and crafts of hiding information that might be leveraged to design novel kinds of privacy-preserving technologies. In this paper, we present findings from our three-month long original ethnographic work with various communities in Dhaka, Bangladesh that reveal a wide range of culturally embedded techniques of hiding confidential and sensitive information from their 'others'. Our analysis demonstrates the dynamic nature of these techniques, the learning process associated with them, and their deep relationship with contextual politics that these communities are embedded in. We further connect our findings to the broader interests of CSCW around otherness, ethics, and democracy, and also discuss their implications for design.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0370.009

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 head0.048
GPT teacher head0.296
Teacher spread0.249 · 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 designQualitative
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

Citations11
Published2019
Admission routes2
Has abstractyes

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Same venueProceedings of the ACM on Human-Computer InteractionSame topicICT in Developing CommunitiesFrench-language works237,207