Conditional constructions, mental spaces, and semantic compositionality
Bibliographic record
Abstract
“It makes me feel like I'm going to cry,” she said. “I can just imagine if it was my daughter.” ( Vancouver Sun , Oct. 4, 2000) [A woman comments on a reported case where a man assaulted a sleeping girl.] Readers of the Vancouver Sun did not sit back and wonder what the speaker thought would have happened “if” her daughter had been the victim of such an assault. She did not have to present a then clause and describe the consequences explicitly. Not only were her actual hearers, and the eventual readers of the paper, able to build up the intended counterfactual situation (marked by the verb was ); they were also presumably able to envision the likely emotional results on a victim's family. Furthermore, they surely recognized that the woman was not primarily expressing specific fear about her own daughter's safety, but empathy with the real-world victim and her mother. How did they do all this, prompted apparently only by the set-up of a situation where the speaker's daughter was imagined to be an assault victim? Conditionals and conditional reasoning There is something about if which engages the curiosity of the analyst. And rightly so: not only is the kind of reasoning manifested in a form such as imagine if it was my daughter an important aspect of human thought, but it also seems uniquely human to imagine in such detail scenarios which may be unreal and perhaps impossible (the speaker need not necessarily have a daughter in actuality), and to reason from them.
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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 | 0.001 | 0.002 |
| 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 | 0.010 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".