Bibliographic record
Abstract
In the unitary design of systems involving people, objects, processes and concepts, the engineering acquires a hybrid character, requiring the coagulation between the contributions based on diverse expertises and posing coordination and communication problems inside mixed design teams. The LORNET project federates research institutions from Canada for the construction of a “middleware” supporting technical and semantic inter-operation between campuses and repositories distributed on the Internet. It is a double challenge: an effort of concurrent research for instrumenting the instructional concurrent engineering. Observing the circle between “doing by learning” and “learning by doing”, activity coordination systems can be enriched with a “semantic layer” (facilitating the procedures execution by alleviating their comprehension and learning). Reciprocally, the production and management of distributed pedagogical activities require collaborative procedure modelling and orchestration. This paper is addressed to those seeking different perspectives in dealing with complexity. I summarize it as follows: mixing the management of persons, objects, processes and knowledge; using the procedures' models for their orchestration; approaching a “4d” vision for extending the observation of short process (“ontogenetic” and “physiological”) to those of long-term evolutions; unitary management of phylogenetic cascades-reproducing structures and processes-based on metafunctions.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".