Bibliographic record
Abstract
Universities as institutions, and scholars as members of those institutions, have played important roles in journalism as knowledge generators, including sources and guest columnists but, usually, at arms-length. This chapter explores how universities and academics are becoming more central to journalism through a case study of The Conversation Canada’s coverage of COVID-19. Funded mostly by universities, The Conversation Canada is a not-for-profit digital journalism organization that challenges journalism’s professional identities, historic practices and commercial orientation. It has met with some institutional wariness from legacy media, reflecting how journalists have tended to react when actors outside the field have taken on activities traditionally associated with the profession. During the pandemic, public interest in COVID-19 supported a significant increase in readership for the digital publication focusing on stories that offered advice, guidance, and how-tos on issues such as the nature of the virus, social distancing, lockdowns, and hoaxes. The increased reach for The Conversation Canada came as more than 182 media outlets reported job cuts and almost 30 community newspapers were closed down across the country. The case study suggests that the higher education sector is in a unique position to provide trusted expertise during global crises. As a model, this case study suggests that partnerships or collaborations between journalism organizations and the higher education sector can be useful ways to mobilize university research and expert information given the market failure for commercial journalism in Canada, and its impact on communications infrastructures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".