New perspectives on Judith Shklar
Bibliographic record
Abstract
In my conversations with students, I often end up asking if they have heard of Judith Shklar.Almost none of them have; she is rarely taught in undergraduate philosophy or politics departments, but she becomes known to many of us once we are a little older and wiser.My own PhD supervisor recommended that I read Shklar in the first weeks of my doctorate, and she has continued to shape and challenge my work ever since.Of course, Shklar has not been "ignored" or "forgotten," but she has remained a niche interest.Most know her for her famous essay "The Liberalism of Fear" (1989) in which she argues in favor of a liberalism centered around a summum malum, rather than a positive doctrine of justice or civic virtue.She is certainly not as wellcelebrated as the other great political theorists and philosophers of her time such as Isaiah Berlin, John Rawls, and Michael Walzer, all of whom she considered friends.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.047 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.013 | 0.032 |
| Insufficient payload (model declined to judge) | 0.014 | 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".