Identifying PLM themes and clusters from a decade of research literature
Bibliographic record
Abstract
Product lifecycle management (PLM) encompasses a wide array of expertise, from designing green products to knowledge representation techniques. This paper characterises PLM as a research domain through the themes and clusters of a decade of scientific literature. Authors' keywords from 1,390 research papers published from 2005 to 2015 are analysed. The co-occurrence of these 2,947 normalised authors' keywords, connected in pairs via 11,289 edges, indicates how PLM research themes relate to each other to form communities - or clusters. These communities are revealed by filtering the network according to the weights of the network's edges. The PLM core cluster, the PLM global cluster and the PLM overall cluster are distinguished based on the level of filtering, thus unveiling increasing levels of detail. The four major communities composing the PLM global cluster are 'interoperability', 'ontology', 'product data management' and 'lifecycle assessment'. The PLM overall cluster also reveals the 'intelligent product' community, which relates to the Industry 4.0 phenomenon. The BIM community is revealed as well, but remains isolated from the PLM overall cluster.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.059 | 0.052 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".