Wishes before ifs: mapping “fake” past tense to counterfactuality in wishes and conditionals
Bibliographic record
Abstract
Counterfactuals express alternatives that are contrary to the actual situation. In English, counterfactuality is conveyed through conditionals (“If pigs had wings, they could fly”) and wish-constructions (“I wish pigs had wings”), where the past tense morpheme marks non-actuality rather than past temporal orientation. This temporal mismatch seemingly complicates the already challenging task of mapping abstract counterfactual meaning onto these linguistic expressions during first language acquisition. In this paper, we investigated the role of linguistic transparency on the acquisition of different counterfactual constructions with a corpus study on the spontaneous production of English-speaking children between the ages of 2-to-6. We extracted wish-utterances from 52 corpora available on CHILDES to compare children’s wish productions with those of adults, and additionally extracted counterfactual conditional utterances for 6 children to provide a comparative longitudinal overview of counterfactual wishes and conditionals. Our results support the idea that complexity of form-to-meaning mapping influences the emergence of counterfactual language. First, we observed a substantial number of productive errors in children’s speech, where they produce counterfactuals with present tense marking instead of past. These errors are consistent with a stage where children have yet to figure out that the past tense is an obligatory component of English counterfactual constructions signaling a present non-actuality, rather than a past event on the timeline. Second, our results show that wish-constructions, which are linguistically more transparent than counterfactual conditionals, generally emerge before counterfactual conditionals in children’s speech. This suggests that in English, counterfactual wishes might be easier to acquire than counterfactual conditionals.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".