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Record W4238395569 · doi:10.1145/1094855.1094878

Agile environments...

2005· article· en· W4238395569 on OpenAlexaff
Dean Mackie, Gifford Louie, Jason Rogers, Niall Shaw

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBIM and Construction Integration
Canadian institutionsProfessional Engineers Ontario
Fundersnot available
KeywordsAgile software developmentWork (physics)Culture changeOrganizational cultureBusinessEngineering managementComputer scienceKnowledge managementOperations managementEngineeringProcess managementPublic relationsSoftware engineeringSociologyPolitical science

Abstract

fetched live from OpenAlex

This poster tells the story of the introduction of furniture on wheels into a very traditional corporate culture. We've all heard of innovative office environments used by design firms, startups, and skunk works, but what about the rest of us? At a 90-year-old $80-Billion financial institution we attempted to implement and measure the effects of an office environment that would be a logical extension of our existing culture, and would also better support collaborative work and the use of Agile Software Development methodologies. A cross-functional project team received new office furniture on wheels which allowed more team interaction and fast layout reconfiguration. The economics and corporate culture effects of this move were recorded. The team was surveyed on ergonomics and ability to collaborate six months before and six months after the change, and the results compared with a team that did not receive the change over the same period. While the sample size was too small to imply universal results, it did anecdotally indicate the benefits of continuing and expanding the implementation.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.108
Threshold uncertainty score0.362

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0050.004
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1080.089

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 head0.004
GPT teacher head0.166
Teacher spread0.162 · 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 designNot applicable
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

Citations0
Published2005
Admission routes1
Has abstractyes

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