Bibliographic record
Abstract
In this essay, Joseph Carens responds to the other articles, primarily by exploring methodological issues. In particular, he argues that what appear to be disagreements between the other authors and him are often really differences in the questions they are asking and in the presuppositions they are adopting. He contends that this is especially clear with regard to the pieces by Tom Malleson and Jim Johnson, both of which address questions about egalitarian political economy and the uses of markets. He endorses Arash Abizadeh’s defence of an open borders view, simply noting a difference in their theoretical approaches. He unpacks Sarah Song’s objection to the analogy he drew between feudal privilege and the advantages enjoyed by people in rich states today, showing why it is unclear whether he and she really disagree. He argues that Rainer Bauböck’s action-guiding approach to political theory is an alternative to his own approach that has its own disadvantages and limitations. He agrees with Courtney Jung’s argument that the foreseeable effects of climate change on mobility do not undermine his open borders argument. He concludes by expressing deep appreciation for the ways in which the article by Simone Chambers and the one by Kiran Banerjee and Abe Singer bring into view the complexities and challenges entailed by the practice of political theory.
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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 | 0.036 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.063 |
| Scholarly communication | 0.025 | 0.027 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.041 | 0.081 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".