Bibliographic record
Abstract
In 2008, the Arctic institute won an International Polar Year (IPY) grant, through Indian and Northern Affairs Canada, to promote Arctic science to Canadians. The award was timely for a number of reasons. For one, the Arctic is currently the focus of much public attention because of the obvious and massive impact of climate change. In the past decade, sea ice has broken extent minimums three times. Scientists suspect that some polar bear populations are in danger because of lost habitat. Warming temperatures are causing vegetation changes on mountain slopes and on tundra. Anyone at all familiar with the Arctic knows this list could go on. In addition, more attention is currently being paid to the endeavor of communicating science to the public. Governments, scientists, and communication professionals are all making an increased effort to present research results to the public—albeit for very different reasons. There’s even a small but growing cadre of journalists dedicated to science news. And of course, the Arctic science promotion program falls at the end of the fourth IPY, when many science projects are beginning to return results. Yet the landscape is not without its valleys. Layoffs are gutting newsrooms in Canada and the United States, and science reporters are among the first casualties. Some evidence shows the public’s interest in science is waning. Traditional media are losing audiences to non-traditional sources such as websites, blogs, and other forms of social media. This paper will offer a short discussion of the Arctic Institute’s IPY project in the context of the wider movement to promote and publicize science.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.000 | 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 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".