The construction of the Covid-19 pandemic as a social problem: expert discourse and representational naturalization in the mass media during the first wave of the pandemic in Canada
Bibliographic record
Abstract
In this paper, we analyze the evolution of the expert discourse in the media during the first wave of the Covid-19 pandemic in Canada. From our analysis of 527 media products published by CBC/Radio Canada between January 1 and August 31, 2020, it was possible to document the type of expertise mobilized, the types of experts engaged by the media, the modalities of appropriation of this discourse by non-experts and the use of expert discourse by political actors. We organize our analysis around governmental measures that have generated more controversy and debate in the media (e.g., closing international borders) and that will be used to analyze the processes of representational naturalization (Negura and Plante, submitted). We begin our chapter with an overview of the use of expertise in the Canadian public- health decision-making chain in the context of the Covid-19 pandemic by highlighting the tensions, contradictions, and paradoxes in political communication that this process revealed. We demonstrate the relevance of studying these dynamics reflected in the media from the perspective of social representations. A brief explanation of the research objectives, the data used and some methodological elements will follow. We then discuss the results of our analysis of the different stages of the evolution of the pandemic in Canada according to the expert discourse in the media. Finally, our analysis focuses on the role of expert discourse in determining what aspects of Covid-19 the public and the political authorities in Canada have defined as a social problem.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".