Bibliographic record
Abstract
This paper challenges the cross-linguistic validity of the tense–aspect category ‘perfect’ by investigating 15 languages from eight different families (Atayal, Brazilian Portuguese, Dutch, English, German, Gitksan, Japanese, Javanese, Korean, Mandarin, Niuean, Québec French, St’át’imcets, Swahili, and Tibetan). The methodology involves using the storyboard ‘Miss Smith’s Bad Day’ to test for the availability of experiential, resultative, recent-past, and continuous readings, as well as lifetime effects, result-state cancellability, narrative progression, and compatibility with definite time adverbials. Results show that the target forms in these languages can be classified into four groups: (a) past perfectives; (b) experientials; (c) resultatives; and (d) hybrids (which allow both experiential and resultative readings). It is argued that the main division is between past perfectives, which contain a ‘pronominal’ tense, on the one hand, and the other three groups on the other, which involve existential quantification, either over times (experiential) or over events (resultative). The methodological and typological implications of the findings are discussed. The main conclusion of the study is that there is no universal category of ‘the perfect’, and that instead, researchers should focus on identifying shared semantic components of tense–aspect categories across languages.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.067 | 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 teacher head, 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".