Epistemic Indefinites: Are We Ignorant About Ignorance?
Bibliographic record
Abstract
Epistemic indefinites make an existential claim and convey that the speaker does not know which individual makes this claim true. The account put forward by Aloni and Port [3, 2] which we will dub the Lack of Relevant Identification Approach defends that not knowing who means that the speaker cannot identify the individual that satis es the existential claim in a contextually relevant way. The Variation Approach (see, e.g., Alonso-Ovalle and Menendez Benito [5], Chierchia [7], Falaus [9], Giannakidou and Quer [16]) assumes that not knowing who means that that individual is not the same in all of the speakers epistemic or doxastic alternatives. In this paper, we will argue that the behaviour of Spanish algun presents challenges for both approaches. Our conclusion will be that we still lack a clear understanding of what the ignorance component conveys.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.034 |
| Scholarly communication | 0.008 | 0.031 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".