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Record W2588743120 · doi:10.1145/2998181.2998360

Using the Model of Regulation to Understand Software Development Collaboration Practices and Tool Support

2017· article· en· W2588743120 on OpenAlex
Maryi Arciniegas-Mendez, Alexey Zagalsky, Margaret‐Anne Storey, Allyson F. Hadwin

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
TopicSoftware Engineering Techniques and Practices
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceWork (physics)Software developmentSoftwareAction (physics)InterviewKnowledge managementVocabularySoftware engineeringProcess managementEngineering

Abstract

fetched live from OpenAlex

We developed the Model of Regulation to provide a vocabulary for comparing and analyzing collaboration practices and tools in software engineering. This paper discusses the model's ability to capture how individuals self-regulate their own tasks and activities, how they regulate one another, and how they achieve a shared understanding of project goals and tasks. Using the model, we created an "action-oriented" instrument that individuals, teams, and organizations can use to reflect on how they regulate their work and on the various tools they use as part of regulation. We applied this instrument to two industrial software projects, interviewing one or two stakeholders from each project. The model allowed us to identify where certain processes and communication channels worked well, while recognizing friction points, communication breakdowns, and regulation gaps. We believe this model also shows potential for application in other domains.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.717
Threshold uncertainty score0.369

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 communication0.0000.001
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 head0.133
GPT teacher head0.360
Teacher spread0.227 · 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