o wt oS a yG o o d b y et ot h eT h i r dM a n
Bibliographic record
Abstract
this paper was conceived and written. We would like to thank Mohan Matthen, Julius Moravcsik, Sandra Peterson, and Nathan Tawil for insightful discussions about the content of the paper. We would also like to thank two anonymous referees for thoughtful comments. Pelletier also wishes to acknowledge the McCalla Professorship that he received from the University of Alberta, during which time this paper was written. Francis Jeffry Pelletier and Edward N. Zalta menides, also shows how the distinction points to an ambiguity in one of the principles that plays a key role in the Third Man argument. This was the focus of her paper "Goodbye to the Third Man" (1992). In the present paper, we examine this application to the Third Man; for, though many scholars acknowledge that a distinction in modes of predication helps us to understand the second half of e r ei s not widespread agreement about what the distinction really amounts to and whether it leads to a solution of the Third Man. For example, Durrant (1997) thinks Meinwald's work is `seminal' but is developed in the wrong direction. He is not opposed to the distinction as such, but argues against its application to self-predicational statements. A second example is Frances (1996), who believes Meinwald's interpretation is `new and perhaps revolutionary' but that the distinction in predication has implications that Meinwald fails to consider. A third example is Sayre (1994), who says that Meinwald's attribution of ta alla/pros to Plato is `convincingly documented' and that "her application of the distinction to the Third Man regress is a major contribution to the literature on that topic" (p. 115). He suspects, however, that the explication in terms of `genus-species' attributions is not as close as she believe...
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.137 | 0.036 |
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".