The management and administration of government communications in <scp>C</scp>anada
Bibliographic record
Abstract
Abstract This article examines the communications function in Canadian government using the results of a pilot study on the governments of Canada, Ontario and the City of Toronto. It first defines what government communication is, explains what activities are included within the function, and then explains how and for what purposes government communications are used. It also provides a high‐level overview of how communications within the federal, provincial and municipal governments are managed and administered. The article concludes with some observations about the nature of government communications in Canada and thoughts about future research in the area. Sommaire Cet article examine la fonction de communication au sein du gouvernement canadien en se fondant sur les résultats d'une étude pilote sur les gouvernements du Canada, de l’Ontario et de la Ville de Toronto. Il définit tout d'abord en quoi consiste la communication gouvernementale, démontre quelles sont les activités que comporte cette fonction, puis explique comment est utilisée la communication gouvernementale et à quelles fins. L'article offre également un aperçu de haut niveau sur la manière dont on gère et administre les communications au sein du gouvernement fédéral et des gouvernements provinciaux et municipaux. En conclusion, l'article présente certaines observations sur la nature de la communication gouvernementale au Canada et des suggestions de recherche future dans le domaine.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".