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Record W2104844656 · doi:10.1145/1806799.1806854

Awareness 2.0

2010· article· en· W2104844656 on OpenAlex

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 Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceTask (project management)PrioritizationProcess (computing)Process managementKnowledge managementEvent (particle physics)Software project managementProject managementProject teamSoftware development processSoftware developmentSoftwareSystems engineeringEngineeringSoftware construction

Abstract

fetched live from OpenAlex

Software development teams need to maintain awareness of various different aspects ranging from overall project status and process bottlenecks to current tasks and incoming artifacts. Currently, there is a lack of theoretical foundations to guide tool selection and tool design to best support awareness tasks. In this paper, we explore how the combination of highly configurable project, team and contributor dashboards along with individual event feeds is used to accomplish extensive awareness. Our results stem from an empirical study of several large development teams, with a detailed study of a team of 150 developers and additional data from another four project teams. We present how dashboards become pivotal to task prioritization in critical project phases and how they stir competition while feeds are used for short term planning. Our findings indicate that the distinction between high-level and low-level awareness is often unclear and that integrated tooling could improve development practices.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.686
Threshold uncertainty score0.341

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 science0.0010.000
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 head0.016
GPT teacher head0.283
Teacher spread0.266 · 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

Citations117
Published2010
Admission routes1
Has abstractyes

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