Bibliographic record
Abstract
Why should Canadians, Australians or anyone else care about human rights atrocities, health epidemics, environmental catastrophes, weapons proliferation or any other problems afflicting faraway countries when they do not have any direct or immediate impact on our own physical security or economic prosperity, viz. our traditionally defined national interests? Are concerns about ‘value’ issues just optional add-ons to states’ foreign policy? This paper spells out my long-held belief, which has its origins in the Pearsonian liberal tradition, that there is a third kind of national interest which every country should pursue: being, and being seen to be, a good international citizen. My argument – which I illustrate with reference to issues such as nuclear disarmament, aid policy, the treatment of asylum seekers, and the responsibility to protect populations against genocide and other crimes against humanity – is that acting as a good international citizen wins hard-headed reputational and reciprocal-action returns, and as such bridges the gulf between idealism and realism by giving realists good reasons for behaving like idealists.\n(This paper was adapted from the author’s lecture, delivered in his capacity as the 2016–17 Simons Visiting Chair in International Law and Human Security, Simon Fraser University, Vancouver, 15 September 2016.)
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.040 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".