The Stanfield Conversations: The US election and democracy's global fate
Bibliographic record
Abstract
In 2024, more than half the world's population went to the polls. Rather than serving as an emblematic year for democracy, these elections suggest that democracy is at a crossroads, with many countries experiencing democratic erosion or shifts toward autocratic rule. The US presidential election exemplified this trend, raising questions about the resilience of democratic institutions. These concerns were the focus of the annual Stanfield Conversations at Dalhousie University, where CBC Radio host Piya Chattopadhyay moderated a discussion between renowned international affairs journalist Doug Saunders and Canada Research Chair in Racial Inequality in Democratic Societies Dr. Debra Thompson on the global ramifications of the US election and the broader challenges facing democratic governance. In this essay, I analyze their key insights, exploring the pressures threatening democracies worldwide and the specific implications for Canada's political landscape.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.016 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 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".