The Challenges of Creating a Bilingual (English-Italian) E-Dictionary of Sports and Games Terminology
Bibliographic record
Abstract
The aim of this paper is twofold. It firstly intends to provide an overview of the existing bilingual English-Italian dictionaries of sports and games terminology, in order to illustrate their main features and consider how this area of ESP (English for Special Purposes) lexicography may be improved. In the second part, a hypothetical e-dictionary entry is presented, taking as an example the lemma padel, indicating a new discipline that has become very popular in the past few years. The advantages of using online multimodal and multimedial dictionaries are discussed, while also making reference to the challenges of creating them: on the one hand, they make it possible to incorporate a plethora of information also via external hyperlinks, which makes them more dynamic resources with an encyclopedic character, but on the other hand it is necessary to constantly update dictionaries as hyperlinks ‘age’ over time and new disciplines appear. The inclusion of authentic material may also be problematic due to copyright reasons.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.015 |
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".