Mediatizing the COVID-19 Pandemic: International Perspectives
Bibliographic record
Abstract
Since its outbreak in 2020, the COVID-19 pandemic has upended ways of life all over the world, transforming itself into a medical, social, and political event all at once (Agartan, Cook, & Lin, 2020; Bobba & Hubé, 2021; Banque mondiale, 2020).Over the past three years, we have had to learn how to live in an environment reconfigured by the uncertainty resulting from the eruption of a public health crisis of which repercussions have been felt in all areas of social life.In such a context, information and communication flows take on a key role in establishing and highlighting ways of seeing this problem.Such flows, whether they come from private, public, or institutional sources, have proven themselves to be invaluable resources, enabling citizens to cope with challenges posed by an unfamiliar and anxiety-inducing situation.In this sense, informational and communicational processes take part at different levels in the chain of mediations of the social order while increasing social tensions through media processes that are becoming more and more complex.These processes are influenced by a "media logic" (Esser & Strombäck, 2014) that contributes to a renewal of norms as well as of organizational modes and habits relative to information and communication.At a time when our societies have become hypermediated, the COVID-19 pandemic has shown how and through what means Cesar, Camila M reira, Giass n, Thierry, & Dum uchel, David.
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 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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".