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3.5.2 Continuous Appraisal Method (CAM)… A New Paradigm for Benchmarking Process Maturity

2000· article· en· W2150651864 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

VenueINCOSE International Symposium · 2000
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsLockheed Martin (Canada)
Fundersnot available
KeywordsMaturity (psychological)Capability Maturity ModelBenchmarkingProcess (computing)EngineeringProcess managementService Integration Maturity ModelEngineering managementComputer scienceSystems engineeringSoftwareManagementOperating system

Abstract

fetched live from OpenAlex

ABSTRACT The publication of EIA/IS 731.1 Systems Engineering Capability Model (SECM) establishes a standard against which an organization determines its level of process maturity for systems engineering. EIA/IS 731.2 SECM Appraisal Method provides guidance on the conduct of an appraisal using the SECM. The EIA/IS 731.2 method of derterming the process maturity level is based upon the Software Engineering Institute's (SEI) CMM® (Capability Maturity Model) Based Assessment for Internal Process Improvement (CBA IPI) that has been the engineering process maturity appraisal method of choice since its first use in 1994. This paper presents an alternative method for appraising an orgnaization's engineering maturity level—the Continuous Appraisal Method (CAM). CAM provides a significant advantage over the CBA IPI in three areas. First the cost of using CAM is significantly lower than using CBA IPI. Second, CAM allows considerable felexability in scheduling program time during the appraisal. Third, CAM becomes an integral part an organization's process improvement initiative by providing timely feedback on process improvement opportunities.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.715
Threshold uncertainty score0.740

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 head0.007
GPT teacher head0.296
Teacher spread0.289 · 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