MétaCan
Menu
Back to cohort
Record W2985723853 · doi:10.1145/3311957.3359441

Mapping the "How" of Collaborative Action

2019· article· en· W2985723853 on OpenAlex
Christine T. Wolf, Julia Bullard, Stacy Wood, Amelia Acker, Drew Paine, Charlotte P. Lee

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSociotechnical systemComputer-supported cooperative workCrowdsWorkflowProcess (computing)Computer scienceData scienceSpace (punctuation)Collaborative softwareFocus (optics)Knowledge managementSociologyEngineering

Abstract

fetched live from OpenAlex

Process has been a concern since the beginning of CSCW. Developments in sociotechnical landscapes raise new challenges for studying processes (e.g., massive online communities bringing together vast crowds; Big Data technologies connecting many through the flow of data). This re-opens questions about how we study, document, conceptualize, and design to support processes in complex, contemporary sociotechnical systems. This one-day workshop will bring together researchers to discuss the CSCW community's unique focus and methodological toolkit for studying process and workflow; provide a collaborative space for the improvement and extension of research projects within this space; and catalyze a network of scholars with expertise and interest in addressing challenging methodological questions around studying process in contemporary, sociotechnical systems.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.978
Threshold uncertainty score0.064

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.049
GPT teacher head0.310
Teacher spread0.261 · 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

Quick stats

Citations4
Published2019
Admission routes1
Has abstractyes

Explore more

Same topicData Visualization and AnalyticsFrench-language works237,207