Studying Canada in Cuba, Studying Cuba in Canada: A Roundtable Discussion
Bibliographic record
Abstract
Canada and Cuba have a long historical relationship, in governmental and non-governmental realms alike. While hundreds of Canadian students take part in educational exchanges from a variety of Canadian universities, Canadian/Cuban scholarly ties are not as strong as they are in the US or even the UK. There are a handful of internationally recognized Cuba scholars who have been working in Canada for some decades, among them John M. Kirk, Hal Klepak and Keith Ellis. Cuban scholarship in Canada is still notably scant and it cannot really be classified in generational terms. However it is clear that the work of these senior scholars is bearing fruit, as other scholars located in Canada are increasingly working in Cuban Studies, in both teaching and research. A few of these scholars came together recently to discuss their experiences. This isn’t an exhaustive or representative group. The participants in this roundtable conversation include those trained as Cubanists, trained in other fields but with more recent research and/or teaching ties to Cuba, and a Cuban educated in Canada. We came together to discuss what we see as the state of the field in Cuban/Canadian studies today and in the future.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.100 | 0.016 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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".