Le processus de mobilisation de connaissances vers le milieu public : le cas du réinvestissement des résultats de recherche dans l’offre de services aux personnes immigrantes au Québec
Bibliographic record
Abstract
Cet essai est le résultat d’une réflexion critique sur la mobilisation de connaissances en tant que processus. Il fait état d’une expérience de stage effectuée à la Direction de la recherche du ministère du Travail, de l’Emploi et de la Solidarité Sociale (MTESS). Ce stage s’inscrivait dans un ensemble de démarches démarrées par le MTESS en 2015 qui visaient l’identification et la formulation d’une liste d’actions possibles pour améliorer les services offerts aux immigrants et aux entreprises qui les engagent. La définition, la coordination et la réalisation de quatre projets de recherche, puis le réinvestissement de leurs résultats dans l’action publique constitue quelques-unes des démarches mises en place par le MTESS, qui ont donné comme résultat le changement de plusieurs programmes et mesures visant les immigrants et les entreprises qui les engagent. Plus concrètement, cet essai a comme objectifs de présenter et de décrire l’ensemble de ces démarches comme un processus de mobilisation de connaissances (MbC) vers/dans le milieu public; de présenter certaines de leurs retombées sur l’action publique; et d’exposer une réflexion critique sur les activités de mise en circulation de connaissances (transfert, diffusion et vulgarisation), ainsi que sur le métier d’agent d’interface. This essay is the result of a critical reflection about knowledge mobilization as a process. It describes my experience of an internship at Quebec’s Department of research of the Ministry of Labour, Employment and Social Solidarity (MTESS). The internship was part of a series of initiatives started by the MTESS in 2015 that aimed to identify and formulate a list of possible actions to improve the services offered to immigrants and the companies that hire them. The definition, coordination and implementation of four research projects, followed by the reinvestment of their results in public action, are some of the steps which were taken by the MTESS, and that resulted in the transformation of several programs and measures targeting immigrants and companies that hire them. More concretely, the aim of this essay is to present and describe all of these approaches as a process of knowledge mobilization in the public sphere; to describe some of their impacts on public action; and to present a critical reflection on the knowledge dissemination activities (transfer, dissemination and extension of knowledge), as well as on the occupation of "interface agent".
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.023 | 0.019 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".