Bibliographic record
Abstract
Canadian Studies is often described as a conversation, engaged in by all who explore experience and character.1 Ideally, it transcends the partial perspectives of disciplines, and, informed by activist aspirations, bridges the divide between academia and the Canadian community. The record of this conversation and its future agenda were examined closely in the recent millennium issues (Volume 35) of the Journal of Canadian Studies. As Robert Campbell and others explained, the themes animating Canadian Studies - the distinctiveness of the Canadian experience, the uncertain place of Canada in an era of globalization, the relevance of equality, justice, democracy and other noble themes to the Canada project - remain as relevant as ever. In everyday life, of course, we usually talk about more mundane matters - our own splinters of the collective experience. But the last few months have also shown how the shared witnessing of a great and horrendous event can pull Canadians into a joint effort to make sense of unprecedented images and a changed reality. Since September, academics have participated in this, contributing context and meaning on topics ranging from Afghan politics to Islam to national security. Those who study Canada have contributed their share of commentary. Just in the last few days Canadian academics have pronounced on the implications for civil liberties of anti-terrorism legislation, the rediscovered relevance of government as the purveyor of public goods, and the possible impact on architecture of new feelings of urban vulnerability. American efforts to reshape the geopolitical order into a united front against terrorism will only sharpen the challenges for Canada of defining its distinctive role in world affairs, and a distinctive experience worth reporting to the world. But there may be limits to the contribution academics can make to understanding these challenges. In daily conversation, 11 has become the shorthand for the attacks on New York and Washington, and their aftermath. After all, we often prefer to understand history in terms of specific dates. But this preference runs counter to the familiar tendency in academic discourse to focus on larger trends - the deeper currents of change, not the rough waves of daily events. Let me provide an example from my own field, environmental history: while popular writers tend to explain the origins of environmental concerns in terms of specific events (the publication of Silent Spring in 1962; the first voyage of Greenpeace; Earth Day on 22 April 1970), historians usually trace these concerns to less dramatic but more sweeping changes in society and politics, such as increased education, greater opportunities for leisure, and an expanded role for government as the guarantor of public goods such as clean air and water. Similarly, September seems likely to become a touchstone of public memory (where were you on the day. …
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.035 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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".