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Record W2000558260 · doi:10.1504/ijlsm.2011.038601

Distinguishing the indistinguishable: exploring differences in supply chain software packages using centering resonance text analysis

2011· article· en· W2000558260 on OpenAlex
Tim S. McLaren, Priscilla R. Manatsa

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

VenueInternational Journal of Logistics Systems and Management · 2011
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Research
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsDocumentationComputer scienceSoftwareSoftware packageSoftware engineeringSelection (genetic algorithm)Data scienceArtificial intelligenceProgramming language

Abstract

fetched live from OpenAlex

Selecting a Supply Chain Management (SCM) software package is difficult due to the complexity and apparent similarities of the software. This paper uses text mining tools to analyse documentation covering the seven most popular supply chain software packages. The resulting concept maps reveal that any distinguishing features are deeply buried in the documentation, while at a superficial level all seven vendors would appear to address the same concepts. This paper contributes a more precise understanding of the similarities and differences between SCM software packages. Guidelines for using this knowledge to make more rational and informed software selection decisions are discussed.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.817
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.115
GPT teacher head0.285
Teacher spread0.170 · 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