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Record W1532055694 · doi:10.1109/metrics.2004.18

COTS acquisition process: incorporating business factors in COTS vendor evaluation taxonomy

2004· article· en· W1532055694 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

VenueIEEE International Software Metrics Symposium · 2004
Typearticle
Languageen
FieldEngineering
TopicTechnology Assessment and Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVendorProcess (computing)Computer scienceProduct (mathematics)Business processProcess managementRisk analysis (engineering)BusinessMarketingWork in processOperating system

Abstract

fetched live from OpenAlex

The increasingly prevalent use of COTS components has attracted a huge capital pool to the industry. The result is an industry that is characterized by strong change forces and weak resistance. Under such environment, weaker players are constantly replaced by stronger players, and older technologies are constantly replaced by emerging technologies. This phenomenon has brought about a new class of risk to the COTS acquirers. These risk factors include the vendor's financial stability and technology capability. However, the existing COTS vendor evaluation taxonomies remain product centric, focusing only on product functionality and costs. We extend the taxonomies to incorporate business factors in the vendor evaluation process, and the resulting process is called VERPRO. The VERPRO decision making tool, which is based on the analytic hierarchy process, allows the acquirers to incorporate vendor business factors into the selection criteria.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.657
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.028
GPT teacher head0.273
Teacher spread0.245 · 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