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1.4.1 Teaching Old Dogs New Tricks

2004· article· en· W2054154000 on OpenAlexaff
Robert A. Bardo, Phillip Brown

Bibliographic record

VenueINCOSE International Symposium · 2004
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsCapability Maturity Model IntegrationCapability Maturity ModelMaturity (psychological)Consolidation (business)PaceEngineeringCompetitor analysisNew product developmentEngineering managementProcess managementComputer scienceSoftware developmentSoftwareBusinessSoftware development processMarketing

Abstract

fetched live from OpenAlex

Abstract Downsizing, and consolidation of previously independent companies, has produced a clash of cultures in many companies. The problem is exacerbated by “home‐grown” product development cultures that have been around for decades and often fail to adequately integrate new disciplines such as software engineering and systems engineering. Over the years, the pace and complexity of technological development has increased. Mergers have brought together more companies with differing processes. New standards like the Software‐Capability Maturity Model® (SW‐CMM®) and Capability Maturity Model Integration® (CMMI®) have been introduced. All these changes have increased the need for developing and instituting new, integrated, and more repeatable engineering processes. Methods of introducing, and gaining acceptance of, these new processes are becoming very important to the continued competitiveness of many companies. At issue is how best to get a diverse workforce to work together to achieve extraordinary goals. Lockheed Martin Missiles and Fire Control (LMMFC), faced with integrating two former competitors, embarked in 1999 on a course to develop a common product development culture. A primary forcing function was the use of Carnegie Mellon University's Capability Maturity Model‐Integration (CMMI®) standard. The resulting new processes are enabling employees to achieve higher goals – by increasing productivity, maximizing use of information, using proven simplified processes, increasing re‐use, and decreasing re‐work. Results to date indicate a “One Company, One Team” mentality is rapidly becoming the norm at LMMFC.

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 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: Methods · Consensus signal: none
Teacher disagreement score0.865
Threshold uncertainty score0.476

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 science0.0010.000
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 head0.011
GPT teacher head0.269
Teacher spread0.259 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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
Published2004
Admission routes1
Has abstractyes

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