Bibliographic record
Abstract
This chapter builds on Stephen Salkever’s experience as one of the first political theorists to teach comparatively in a serious and sustained way. This work began in the 1980s with a co-taught course comparing ancient Greek and ancient Chinese texts. For Salkever, comparative teaching is a practice of what he calls liberal education, which aims to foster the habits of mind that sustain curiosity and critical self-reflection. Texts are chosen not as representative of a particular culture or tradition but as exemplars of original thinking that unsettled the self-understandings of their authors’ contemporaries as much as they might unsettle ours. In this chapter, Salkever reflects on the contributions of comparative political theory to “deparochializing” and “provincializing” Western political theory and even liberal education as such. Both ways of construing the tasks of comparative political theory see it as a form of “constructive escape” from our received opinions, which can limit our capacity for political judgment if they are left unexamined. Salkever draws out the implications of this way of understanding comparative political theory as an educational practice, highlighting the importance of juxtaposing contradictory texts within a given tradition (to resist cultural essentialism) as well as finding continuities across traditions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".