Bibliographic record
Abstract
Leigh Jenco’s chapter revisits the radical possibility, advanced in earlier work, that transcultural political thought can go beyond dialogue, enabling us to engage as full participants in forms of thought developed by political agents in contexts thoroughly different from our own. Others’ modes of knowledge and knowledge-seeking can become the platforms from which we innovate our own political thought and practice. In this chapter, Jenco argues that the most ambitious way of understanding the project of “deparochializing political theory” goes beyond “decentering” European thought traditions in the way we understand politics, thus making space for non-Western thought as constituent elements in this discipline we call political theory. A more thoroughgoing deparochialization of our discipline would recenter historically marginalized thought traditions as the starting point for critical inquiry and theoretical innovation, eventually yielding new theories that count as knowledge that is relevant to our own sense-making in the world, knowledge for us and not merely about them. Jenco elaborates this Gestalt-shifting approach to cross-cultural theorizing through a discussion of some of its best exemplars, including Stephen Angle’s work on sagehood and Ingrid Jordt’s work on Theravada Buddhism.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".