Chapter 5 Journalism in Canada’s Northern Territories: Digital Media, Civic Spaces, Indigenous Publics
Bibliographic record
Abstract
Every so often the Arctic makes news around the world as scientific research organizations release reports about the state of sea ice extent in the Arctic Ocean.For those who pay attention to climate change news and science, there is a feeling of routine in seeing what the latest variability entails.Text-based descriptions and short stories generally accompany maps that depict the top of the world with a white amorphous blob of frozen Arctic Ocean in the centre of land masses labelled Russia, Canada, Alaska, Greenland and Europe.Outlines on the map show where previous sea ice extended to in prior years.In 2007, media and communications scholars labelled the attention to and panic about sea ice loss a 'media event' as media organizations paid more attention to the depicted losses that were framed as a palpable example of climate change (Christensen, Nilsson and Wormbs 2013).Sea ice loss has continued in a steady but non-linear decline with very little media attention being paid to the peaks and valleys that span the more than a decade of sea ice changes that have occurred since then.Most global publics outside the Arctic know the Arctic through media representations like this: periodic reports that generate spasmodic concern from journalists, editors and their audiences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.026 | 0.011 |
| Scholarly communication | 0.021 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 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".