Freezing out: Legacy media's shaping of AI as a cold controversy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mainstream coverage of artificial intelligence often appears to emphasise the technologies’ benefit and economic potential over its growing downsides. How does a technology poised to be so disruptive become so uncritically embraced? Why is it, simply put, that artificial intelligence's representations in legacy media do not normally convey the controversialities otherwise found in research or policy debates? We introduce the concept of ‘freezing out’ to describe processes of translation that cool down debates over the merits of technology. Freezing out looks at the other side of controversy studies to study the production of uncontroversies or cold controversies rather than hot topics and debates. We use the coverage of artificial intelligence in Canadian national news outlets to analyse how controversiality becomes ‘frozen out’. Since Canadian academics won the prestigious ImageNet prize in 2012 introducing the modern turn toward machine learning approaches, Canada has promoted itself as a global leader. Using in-depth interviews with Francophone and Anglophone journalists as well as topic modelling on data collected from five major newspapers, we find that routine news making processes between journalists, experts, entrepreneurs, and governments build, maintain, and promote Canada's artificial intelligence ecosystem. Freezing out contributes to a broader interest in how heterogeneous actors traverse their domain of expertise across policy, media, and research circles to cool down artificial intelligence controversies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it