Dualité des rôles de chercheur et d’agent d’interface pendant la phase de transfert et de mobilisation des connaissances
Bibliographic record
Abstract
As part of my master's work aimed at evaluating skills and understanding the innovation management needs of regional companies, I was allowed to experience, as part of my internship, the duality of researcher roles and intermediary agent. This internship was structured around two main objectives: to identify and understand the situation of Trois-Rivières SMEs in terms of innovation management * and to contribute to the establishment of a forum aimed at involving the actors ** and making them dialogue. In short, the results of this contextualized action-research project were intended to allow both reflexivity on the organizational dynamics of the practitioners involved and a relevant look at a socio-economic issue of the regional innovation ecosystem (ERI) of Trois-Rivières, in this case the capacity of SMEs to innovate. \nThis essay offers a critical reflection on the duality of roles as researcher and interface agent during the phase of knowledge transfer and mobilization. More specifically, this essay focuses on the main factors that influence a researcher to limit himself to his body of knowledge rather than opening his thinking and analysis to the knowledge of his partners. Demonstrating a keen interest in the question of the research AND action duality, it was therefore possible for me to study this problem within the framework of a collaborative research project. Therefore, it becomes interesting to highlight the intimate relationship that exists between the research thematic and the different roles that the researcher / interface agent assumes throughout such a project (developing, transferring and mobilizing knowledge, animate popularization events, etc.). For example, can we, or even should we, agree to “simplify” theoretical notions when the latter are essential to the concepts and knowledge transferred? What motivation drives the researcher and the partners to collaborate and move forward in a research project? How do we prioritize and coordinate the scientific and practical objectives of the project? \nWithout fully answering these questions, this reflective work will provide a brief and preliminary analysis on the subject. Nonetheless, it will help guide researchers, interface agents as well as researchers who assume the role of interface agent in their thinking about their posture and the roles they will have to assume.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".