La terminologie du domaine de l'intelligence artificielle : néologie et pluridisciplinarité
Bibliographic record
Abstract
The rapid evolution of society continually leads to the emergence of new specialized fields, among which artificial intelligence (AI) stands out as an omnipresent topic in today’s world. This article explores the terminology of AI through an analysis of the bilingual glossary Une intelligence artificielle bien réelle : les termes de l’IA, available on the Grand dictionnaire terminologique website (as an official terminological resource of the Quebec government). This article aims to illustrate, through numerous examples, how AI-related terms are formed and used. It emphasizes the multidisciplinary nature of the terms as well as their linguistic motivation (including both semantic and morphological motivation). The essence of the semantic motivation of AI terms mainly lies in metaphors and metonymies, while morphological motivation primarily involves affixation, which is a crucial method in the creation of new terms. In the case of complex terms, which are more prevalent than simple terms, the article discusses the role of the most frequently used method for abbreviating terms in the field of AI, namely acronymization. The article also highlights the influence of the English language on AI terminology, which includes a significant number of literal translations (calques), reflecting the trend toward the internationalization of technical terminologies and the impact of intercultural exchanges on the evolution of specialized terms.
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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 | 0.001 | 0.001 |
| 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 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".