Truth be told: a corpus-based study of the cross-linguistic colexification of representational and (inter)subjective meanings
Bibliographic record
Abstract
Abstract The study of crosslinguistic variation in word meaning often focuses on representational and concrete meanings. We argue other kinds of word meanings (e.g., abstract and (inter)subjective meanings) can be fruitfully studied in translation corpora, and present a quantitative procedure for doing so. We focus on the cross-linguistic patterns for lemmas pertaining to truth and reality (English true and real), as these abstract meanings been found to frequently colexify with particular (inter)subjective meanings. Applying our method to a corpus of translated subtitles of TED talks, we show that (1) the abstract-representational meanings are colexified in patterned ways, that, however, are more complex than previously observed (some languages not splitting a ‘true’-like from ‘real’-like terms; many languages displaying further splits of representational meanings); (2) some non-representational meanings strongly colexify with representational meanings of ‘truth’ and ‘reality’, while others also often colexify with other fields.
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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".