Bibliographic record
Abstract
How do dictionaries of national biography fit into the context of transnational historical studies?This question is particularly apposite at a time when the very idea of transnationalism is being challenged by the resurgence of atavistic and frequently intolerant forms of nationalism, exemplified in varying degrees by the British withdrawal from the European Union, the election of Donald Trump as President of the United States of America, the politics of leaders such as Vladimir Putin, Tayyip Erdogan, and Viktor Orbán, and the rise of extreme right-wing parties in western Europe.As the post-World War II liberal order threatens to unravel, it is perhaps worthwhile remembering that there is nationalism, and then there is nationalism.'When nationalism stunts the growth, and embitters the generous spirit which alone can produce generous and enduring fruits of literature', wrote the Irish Canadian politician and poet Thomas D'Arcy McGee in 1867, 'then it becomes a curse rather than a gain to the people among whom it may find favour, and to every other people who may have relations with such a bigoted, one-sided nationality.' 1 The kind of nationalism that McGee endorsed had been expressed by his mentor, Charles Gavan Duffy, more than two decades earlier:1
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
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".