Dix conseils pour monter votre fab lab, inspirés de l’expérience d’implantation du fab lab de Brossard
Bibliographic record
Abstract
Que ce soit en milieu académique, communautaire ou public, ou encore en tant qu’incubateur de PME ou qu’atelier d’artiste, les fab labs sont de plus en plus présents et accessibles pour les citoyens, et titillent la curiosité de tous, des plus petits aux plus grands. Qu’est-ce qu’un fab lab ? Que peut-on y faire ? Pourquoi un fab lab en bibliothèque ? Au-delà des questions des usagers, les professionnels des sciences de l’information s’interrogent également quant à la mise en place d’un tel espace. Quels outils et équipements acquérir ? Quel type de personnel y affecter ? Quelle offre de services y présenter ? Pour quelles clientèles ? Comment évaluer les retombées du fab lab auprès de ses usagers ? En se basant sur notre expérience d’implantation du fab lab à la bibliothèque Georgette-Lepage de Brossard, nous avons formulé dix conseils pour aménager ce type d’espace.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.057 | 0.012 |
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".