Bibliographic record
Abstract
The Greek conditional construction ἐὰν μή is usually translated into English using unless, which is a portmanteau combining the ideas of a conditional if and a negative not. Sentences containing ἐὰν μή ‘unless’ can often be challenging to translate for a combination of reasons: 1) in the majority of cases, the usual order of protasis (conditional clause) and apodosis (consequence clause) is reversed; 2) typically, both clauses are negative (or the protasis is negative and the apodosis is a rhetorical question expecting a negative response); 3) at the pragmatic level, the protasis usually describes the only situation or fact that would invalidate the apodosis. In this paper I will show that in many cases conditional sentences with ἐὰν μή ‘unless’ can be rephrased by removing the negative elements in both clauses and making explicit the pragmatic idea of exclusivity. However, this type of rephrasing is not always appropriate, and I discuss a number of situations in which it should potentially be avoided.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".