Mental space embeddings, counterfactuality, and the use of <i>unless</i>
Bibliographic record
Abstract
Unless -constructions have often been compared with conditionals. It was noted that unless can in most cases be paraphrased with if not , but that its meaning resembles that of except if (Geis, 1973; von Fintel, 1991). Initially, it was also assumed that, unlike if -conditionals, unless -sentences with counterfactual (or irrealis) meanings are not acceptable. In recent studies by Declerck and Reed (2000, 2001), however, the acceptability of such sentences was demonstrated and a new analysis was proposed. The present article argues for an account of irrealis unless -sentences in terms of epistemic distance and mental space embeddings . First, the use of verb forms in irrealis sentences is described as an instance of the use of distanced forms, which are widely used in English to mark hypotheticality. In the second part, the theory of mental spaces is introduced and applied to show how different mental space set-ups (in conjunction with distanced forms) account for the construction of different hypothetical meanings. The so-called irrealis unless -sentences are then interpreted as a number of instances of mental space embeddings. Finally, it is shown how the account proposed explains the fact that some unless -constructions can be paraphrased only with if not while others only with except if .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".