After the Ice Age: The effects and implications of federal media policy changes on Northern science communication and the Northern science-policy interface during the Harper era
Bibliographic record
Abstract
A rise in global temperatures in tandem with polar amplification means that the Arctic is warming at a rate three times that the rest of the earth. This is occurring with major implications for the region and is accompanied by a need for adaptive governance and policy initiatives informed by robust science. From 2006 to 2015, the Canadian government, led by Prime Minister Stephen Harper, frequently asserted the importance of the Arctic, speaking to the environmental challenges facing the region. Consistent with this, the Government of Canada invested substantial resources in both Northern science and Northern research infrastructure as demonstrated by its support of polar projects such as the Polar Environment Atmospheric Research Laboratory, the Canada High Arctic Research Station and the 2007-2008 International Polar Year. In early 2008, changes to federal departmental communications and media policies, controlling how federal scientists were contacted and communicated with journalists, prompted the first article of what would become a decade long fixture in the Canadian news media: the muzzling of Canada’s federal scientists. From 2008 to 2015, the ‘muzzling’ of federal scientists was largely narrated by the media and discussed independently of the science communication literature. Notwithstanding the investigation and conclusions drawn by the Office of the Information Commissioner, no further insight into the federal management of science communication under the conservative government of Prime Minister Stephen Harper was offered. This study demonstrates that federal scientists experienced a significant reduction in capacity to communicate their science to the media as a result of the changes made to departmental communication and media policies. These changes created institutional barriers to the communication of federal science, withholding valuable taxpayer funded science from both the media and consequently, the Canadian public. The science-policy interface as it existed prior to the media policy changes was severely eroded due to a reduction in transparency, trust and the timely delivery of science.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.013 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".