Integració de sistemes en empreses: anàlisi empírica
Bibliographic record
Abstract
Projecte de recerca elaborat a partir d’una estada a la University of Alberta, Canada, Estats Units juliol i novembre del 2007. Moltes empreses tenen implantats sistemes de gestió com el de la qualitat, medi ambient o riscos laborals, però es tractava de saber si els gestionaven separadament o de forma integrada i com duien a terme el procés d’integració. El grup de recerca liderat pel professor Dr. Stanislav Karapetrovic va dur a terme recerca teòrica i bàsica aplicada a l’assegurament de la qualitat, concretament a l’estandardització i integració de sistemes de gestió. La metodologia utilitzada va ser aplicar anàlisi multivariant dels resultats d’una enquesta feta a empreses espanyoles durant l’any 2005 (Karapetrovic et al, 2006), i es tractava d’analitzar si les empreses integraven de la mateixa manera o seguien models diferents que les conduïen a resultats diferents. Els resultats del projecte s’han publicat en diferents revistes de difusió i científiques nacionals, i està en revisió en una revista científica internacional, la Journal of Cleaner Production.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".