A Methodological Proposal for the Study of Semantic Functions across Languages
Bibliographic record
Abstract
After a brief revision of the major currents of thought and grammatical models in the history of CS (Contrastive Studies), a method of analysis suitable for corpus-based descriptive studies across languages is presented and discussed in this paper. As an alternative to translation corpora, the use of comparable corpora is advocated and put into practice in a large-scale research on a particular semantic function, quantification, and its expression in two languages, English and Spanish. The different phases of the process are explained and a summary of achievements is provided. The ultimate purpose of the paper is to contribute to the strengthening of the discipline by offering new results about one pair of languages and by suggesting a methodology that can be broadly applied to different semantic fields and pairs of languages.
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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 | 0.028 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.011 | 0.018 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".