A Semantics for the Particle αν in and outside Conditionals in Classical Greek
Bibliographic record
Abstract
Abstract In this paper, we provide a uni ied semantics for the Classical Greek particle αν in its uses both in and outside of conditional sentences. Speci ically, working within the framework provided by formal semantic treatments of conditionals in Stalnaker (1968); Lewis (1973); Kratzer (1981) and subsequent work, we propose that αν is a universal quanti ier over situations—parts of possible worlds. We also detail the interactions between αν and the tense and mood features in a clause, arguing, for example, that the semantics of αν in combination with a 'fake' past tense morphology (Iatridou 2000), which reflects the presence of an exclusion feature in C, gives rise to a counterfactual implicature. Additionally, we address the issue of the surface distribution of αν in the antecedents of some types of conditionals and the consequents of others and argue that, despite its surface distribution, αν is always merged into the consequent of a conditional but sometimes undergoes displacement such that it appears to be located within the antecedent. Our proposal not only illuminates a complex phenomenon in Classical Greek, but also contributes to the understanding of the morpho-semantics of mood, conditionals, and counterfactuality in natural language.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".