Representation of the Conceptual Field “Education” in National Variants of the French Language
Bibliographic record
Abstract
The paper presents an onomasiological analysis of the conceptual field "Education" exemplified by the vocabulary of the national variants of the French language of France, Belgium, Switzerland, and Canada (Province of Quebec) with the aim to establish the basic nomination principles and identify inter-variational differences within the framework of three sectors of the conceptual field "Education": "Educational institutions (établissement d'enseignement)", "Teaching staff (personnel enseignant)", "Students (étudiants)". The basic principles of nominating in the field of education are identified by considering the internal conceptual structure of the language units under analysis; inter-variational differences and alterations are stated by categorical-and-semantic comparability of the differentiation semes in the nominations of three sectors of the conceptual field "Education". The analysis allows to conclude that there exist generic categorical-and-semantic instances within nomination structures that indicate location of training or teaching, specialty, disciplines studied or taught, age, status of the teacher or student, level and methodology of training. In designation of objects in the sectors of the conceptual field "Education" lexical units get into hypernym-and-hyponym relations, thus categorically every sector is formed around some basic concept that is explicated in a hypernym, general nomination for four territorial variants of French, and a set of hyponyms that are more specific and not alike in the exemplification of general semes. The onomasiological analysis of the conceptual field "Education" reveals intervariational concomitant or alternating features, especially in sectors of "Educational institutions (établissement d'enseignement)", "Students (étudiants)" due to certain social-and-institutional 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".