MOSAIC project: the challenge of sharing the results of unique research.
Bibliographic record
Abstract
One of the major challenges of many funded research projects is, of course, to validate and perpetuate an approach, methodologies and results, as well as trying to maintain this dynamic beyond the project itself. Some of the European funding for types of project such as those dedicated to "Centres of Vocational Excellence" requires participants to make all their results and deliverables freely available. MOSAIC (Mastering Job-Oriented Skillls in Arts and craft thanks to Centres of vocational excellence) is a European ERASMUS plus project involving seven countries and 15 main partners (universities, training centres and companies). The main aim of MOSAIC is to improve the quality of vocational training in the arts and crafts in order to meet the challenges posed by digital, environmental and socio-economic developments, by proposing to generate innovations from three angles: technical, educational and social. The complexity of MOSAIC is reflected in the very architecture of the project. By deciding to bring together seven countries - Armenia, Belgium, Bulgaria, Canada, Finland, France and Italy - and above all by anticipating a possible and relevant dialogue between very different partners: company directors, teachers, researchers, project managers, product designers, communication managers, technology advisers, craftsmen, designers and others, MOSAIC has banked on the possibility of fruitful collaboration, in scientific terms, between researchers and non-researchers. In this context, the question of disseminating the results of the research has taken on a new urgency. While publication in journals and participation in scientific events are obvious for researchers, they are much more complex and less obvious for non-researchers. It is in this sense that MOSAIC's main deliverable should be understood: a European Observatory of Art Professions, i.e. an online platform that will contain all the knowledge developed throughout the project in order to make all the data and deliverables produced during the project available to everyone. Conceived as part of a joint approach, this open-access structure, defined in its specifications as scalable, interactive and dynamic, has a strong desire to break away from a culture of silos where each player is in some way inward-looking. It should enable each of the project's partners to play their part in disseminating the results. It also has the ambition, through its structure, to continue to bring people together long after the end of the project. See this presentation in this video recording.
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 | 0.356 | 0.414 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.011 | 0.024 |
| Scholarly communication | 0.037 | 0.043 |
| Open science | 0.010 | 0.071 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".