Bibliographic record
Abstract
It was in the very first hours of a meeting. A brand-new Associate of Management Sciences (Option Marketing) and I, his first PhD student, discussed in a restaurant near the University Cheikh Anta Diop de Dakar (UCAD). That’s when the idea of the present Model first emerged. This discussion continues at the Marché aux Puces, in our adjoining rooms of the Cité Internationale Universitaire de Paris (CIUP) and at the restaurant of the same Cité...After the emergence of the idea, there followed the process of its maturation and the laying of the milestones of the present model proposal. To complete the reflection and the puzzle of the construction of this model, it took multiple immersions in different appropriate media. In particular, our immersions at the CIRRELT of the LAVAL University of Quebec as well as the links established were particularly fruitful. Today, the Model has grown...So, the proof of the ripening of this Model, since its emergence, starts from a long conception. This design was followed by a step-by-step construction until its completion. This completion is – today – the fruit of this present Model ([7, op. cit.], Montoussé and Waquet (2006), Gueye et al. (2020), Gueye et al. (2020), Gueye et al. (2020).But there is always room for improvement, as nothing here is sacred and definitive. Everything turns and changes.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.045 | 0.011 |
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".