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Record W4405113472 · doi:10.1016/j.procs.2024.11.078

Design and development of a decision support platform for the evaluation of CSR performance measurement for manufacturing SMEs

2024· article· en· W4405113472 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

VenueProcedia Computer Science · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsCégep de RimouskiUniversité du Québec à Rimouski
Fundersnot available
KeywordsComputer scienceCorporate social responsibilityDecision support systemManufacturing engineeringProcess managementArtificial intelligenceBusiness

Abstract

fetched live from OpenAlex

Corporate Social Responsibility (CSR) involves companies considering their activities' economic, environmental, and social impacts. Implementing CSR processes, including managing numerous standards and performance indices, is particularly challenging for Small and Medium Enterprises (SMEs) due to resource constraints. To address this, we developed an IT platform for self-diagnosis and measurement of CSR performance in manufacturing SMEs. This platform allows managers to evaluate the maturity of their company's CSR and to define improvement strategies by highlighting strengths and weaknesses. The system was designed using Unified Modeling Language (UML) modeling and the ICONIX method, with Dart and Flutter for the front end, Python's Django framework for the back end, and MySQL for database management. The SME-specific CSR performance measurement grid, derived from previous work, structured the platform's forms, ensuring comprehensive performance evaluation and alignment with industry standards.

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)0.008
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.991
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
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 science0.0000.000
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 head0.148
GPT teacher head0.303
Teacher spread0.155 · 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