Analyse de l’image politique de François Legault sur Instagram pendant la crise de la COVID-19
Bibliographic record
Abstract
Background: This article addresses communications on Instagram by Québec Premier François Legault. We were particularly interested in his use of this platform to manage the COVID-19 crisis. For this article, we carried out a content, discourse, and visual analysis of the publications on the Instagram account of the Premier of Québec during two key periods, the beginning of the COVID-19 crisis from March 12 to April 13, 2020, and its continuation from January 6 to February 8, 2021. Analysis: We draw on a content, discourse, and visual analysis of the Premier’s messages on the Instagram platform to understand the strategies used by the Québec government on the Premier’s Instagram feed during the COVID-19 crisis. Conclusions and implications: Through this study, we show the uses of Instagram and how this platform was leveraged to communicate during the COVID-19 pandemic. Legault responded to the urgency of crisis management by showing strong leadership and staying on message.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".