La transformation numérique et l’évaluation de la maturité numérique : des enjeux du ministère de la Culture et des Communications du Québec : étude exploratoire des initiatives au Canada (Fédéral et Ontario), au Royaume-Uni et en Australie
Bibliographic record
Abstract
Le présent essai fait état d’une expérience de stage réalisée dans le cadre du programme de maitrise Pratiques de recherche et action publique du Centre Urbanisation Culture Société de l’Institut national de la recherche scientifique (INRS). Réalisé au ministère de la Culture et des Communications du Québec (MCC), ce stage avait pour objectif de repérer les meilleures pratiques en matière de transformation gouvernementale numérique et d’évaluation de la maturité numérique, et ce, dans le but d’alimenter les réflexions du ministère quant à son propre processus de transformation en ce domaine. Pour ce faire, nous avons privilégié une méthodologie qualitative en réalisant une recension et une étude de la documentation gouvernementale (dite « littérature grise ») de trois États ― le Canada et l’Australie, au niveau fédéral, ainsi que le Royaume-Uni ― et d’une province canadienne, l’Ontario. Cet essai met d’abord l’accent sur la problématique et les principales balises de notre projet de stage. Puis, il présente les résultats de notre recherche et le bilan de nos activités de transfert de connaissances en milieu de pratique et en milieu scientifique. Enfin, il propose un bilan critique de notre expérience de stage ainsi qu’une réflexion sur la mobilisation des connaissances et le métier d’agent d’interface. \n<br /><br /> \nThis essay reports on an internship experience completed as part of the Research Practices and Public Action (Pratiques de recherche et action publique) Master’s program at the Institut national de la recherche scientifique (INRS) Urbanisation Culture Société Research Centre. The objective of this internship, which was completed at the Ministère de la Culture et des Communications du Québec (MCC), was to identify best practices in government digital transformation and digital maturity assessment. The findings are intended to inform the ministry’s own digital transformation process. The process involved using qualitative methods to study and review government documentation (known as “grey literature”) from three States (the federal governments of Canada and Australia, as well as the government of the United Kingdom) and one Canadian province (Ontario). This essay begins by outlining the focus and main guidelines of our internship project. It then presents the results of our research and the assessment of our knowledge transfer in practice and scientific settings. Finally, it offers a critical assessment of our internship experience as well as a reflection on knowledge mobilization and the profession of knowledge broker.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".