Bibliographic record
Abstract
Left political philosophers have adopted a vast array of theoretical perspectives for diagnosing the ills of contemporary capitalist societies and they offer a plethora of different prescriptions for remedying these injustices. In this chapter I critically assess another principled account of justice that functions at the level of ideal theory - left-libertarianism. ‘ Left-libertarian theories of justice hold that agents are full self-owners and that natural resources are owned in some egalitarian manner’ (Vallentyne and Steiner, 2000b, p. 1). Debates concerning the viability of left-libertarianism as a political theory are beginning to gain momentum. Barbara Fried (2004, 2005) argues that one of the pillars of left-libertarianism - self-ownership - is an indeterminate concept and that left-libertarianism is indistinguishable from liberal egalitarianism. Mathias Risse (2004) argues that left-libertarianism is incoherent. Left-libertarians have retorted (Vallentyne, Steiner and Otsuka, 2005) by arguing that their theory is coherent, determinate and relevant. For the most part, the central focus of these spirited debates has been on the philosophical underpinnings of left-libertarianism rather than on its practical prescriptions. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".