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Record W4382776040 · doi:10.1093/jcmc/zmad015

Transparency, openness and privacy among software professionals: discourses and practices surrounding use of the digital calendar

2023· article· en· W4382776040 on OpenAlexaboutno aff
Vanessa Ciccone

Bibliographic record

VenueJournal of Computer-Mediated Communication · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsSociotechnical systemTransparency (behavior)SubjectivitySituatedPublic relationsEthnographySociologyOpenness to experienceKnowledge managementPsychologySocial psychologyPolitical scienceComputer scienceEpistemologyComputer security

Abstract

fetched live from OpenAlex

Abstract Research on the groupware calendar system (GCS) has sought to understand its situated use in workplace contexts, revealing insights around design, culture, and self-understanding. A critical look at how knowledge workers use the GCS, and conceptualize of this use, reveals often overlooked sociotechnical values that figure prominently in workers’ lives. At a time when the public–private entanglement has become top-of-mind, this article adds to research on the GCS and professional subjectivity. It shows how organizational values circulate through use of the GCS and explores how hierarchy is negotiated on it, in part through design. It finds that senior-level workers are afforded opportunities to make their calendars private, while nonsenior workers are met with frustration when doing so. The article draws from a multi-sited ethnography, focusing on interviews with software workers in Canada. Findings suggest that the logistical functions of the GCS shape the affective dimensions related to its use.

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)0.034
metaresearch head score (Gemma)0.065
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.978
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0340.065
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0220.048
Scholarly communication0.0130.010
Open science0.0010.015
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.073
GPT teacher head0.384
Teacher spread0.311 · 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.

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

Citations3
Published2023
Admission routes1
Has abstractyes

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