Cross-linguistic categorization of throwing events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Research on cross-linguistic categorization reveals that there were universal principles constraining the categorization of motion events across languages, and variations only distributed in a limited range. However, this finding has not been widely verified across languages and semantic domains. In this paper, we will address whether the universal constraints exist in the cross-linguistic categorization of throwing events, with the data collected with a behavioral approach. We asked 79 adult native speakers of English(12 male, 17 female), Chinese(15 male, 15 female), and German(18 male, 12 female) to perform actions denoted by near-synonymous ‘throw’ verbs in their native languages. Then we coded the features of their actions and compared them across individuals and languages. The results support the finding of previous studies that event categorization is constrained across languages. In addition, the top-down approach we adopted in this study allowed us to capture the focal and extensional semantic range of each verb involved, which advanced our knowledge of event categories and different semantic representations of a class of near-synonyms.
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.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it