Bibliographic record
Abstract
According to anti-individualism, the basic building blocks of the world are not individuals. The anti-individualist argues that standard, individual-entailing claims–for instance, that Theia is a cat–are mistaken in presupposing that there are individuals, but that such claims correspond to statements in a feature-placing language devoid of these presuppositions. Instead, the world is entirely made up of non-individualistic features–structurally akin to familiar examples such as it's raining or it's snowing–that are arranged in particular ways. Since features do not carve out individual differences, however, this seems to entail that there is a class of statements in an individualistic language–statements expressing mere differences in which object is which–the members of which all correspond to the same anti-individualist description. That is, anti-individualism seems to entail anti-haecceitism, according to which all differences in ways the world could be supervene on qualitative differences. In this paper, I argue that, on the contrary, the anti-individualist has the resources to accommodate non-qualitative differences among ways the world could be. Moreover, haecceitistic anti-individualism does justice to the motivations for rejecting individuals while nevertheless accommodating intuitions that certain kinds of scenarios are metaphysically distinct.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.027 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".