Bibliographic record
Abstract
First, let me say that it is a pleasure to be asked to comment on the work of Walker Connor, who is a huge figure in the study of nationalism, and has been tremendously influential for me both personally and in my own work. Let me say something personal, first. In the spring of 2005, Walker Connor came to my home university, Queen's University in Kingston, Canada, as a Fulbright Fellow. This was our first Fulbright Fellow and we were thrilled to have an international star in nationalism join us. He was witty, humorous, smart, and very, very kind. He was also – I was struck by this – very generous with his time, with students, and with junior scholars. He talked at length to our graduate students who were working on nationalism. He told me that he enjoyed students very much, and that in his opinion the American liberal arts colleges were excellent places to work because one could see the impact of one's ideas and challenges on students.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.021 | 0.032 |
| Scholarly communication | 0.017 | 0.024 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.020 | 0.036 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".