Estimating Similarity of Words in the Language Consciousness of Speakers of Different Variants of French (France, Belgium, Canada, Switzerland)
Bibliographic record
Abstract
This article considers a method that helps to evaluate the level of similarity of a word in the language consciousness of the French language speakers from four different francophone regions (France, Belgium, Switzerland, Canada). To illustrate the approach the word vie (life) has been chosen as it belongs to the nucleus of the language consciousness of French speakers, according to the results of the associative experiment that was made in 2008 and 2009 via Internet with the aid of google-questionnaires. To assess similarity in understanding the word vie (life) by French speakers from the chosen regions we use the semantic closeness index finding formula proposed by D. Yu. Prosovetsky. Originally the formula was applied to the calculation of the semantic closeness of two different words. In this research the formula has been adapted to the analysis of one word-stimulus presented in the associative fields of the regions considered. To apply this formula one needs first to count the number of similar words that appear in associative fields of the chosen word pairwise. Six pairs are addressed (France-Belgium, France-Canada, France-Switzerland, BelgiumCanada, Belgium-Switzerland, Canada-Switzerland). After that, the doubled number of similar words in one pair of countries is divided by the sum of the total number of reactions of the two given fields. The calculations conducted in this article pairwise for the word vie (life) present the following numbers of the index of semantic closeness: pairs Belgium-Switzerland, Switzerland-Canada, France-Switzerland 0,09 each; the highest index belongs to the pairs of France-Canada and Belgium-Canada 0,12 each; the pair France-Belgium occupies the intermediary position with the result of 0,1. The calculations illustrate that the associative fields of the word vie in the four considered regions (France, Belgium, Switzerland, Canada) include similar elements, however, the exact agreement is not observed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".