Testing the Development of French Word Knowledge by Advanced Dutch- and English-Speaking Learners and Native Speakers
Bibliographic record
Abstract
The present study is a continuation of the work presented in the 2001 article by Greidanus and Nienhuis. In the current study, we also examine the quality of word knowledge among advanced learners of French as a second language (L2) by means of a word associates test. We studied the development of word knowledge among six groups of university-level participants, who were (a) native speakers of French and (b) learners of French as a foreign language with two different first languages (L1s), Dutch and English. The format of the test differed from that used in the 2001 Greidanus and Nienhuis study as follows: (a) the tested words were less frequently used French words; (b) the participants were native speakers of French in addition to two categories of advanced learners of French; (c) the number of associate words (fixed or not) was an independent variable. The findings showed that both native and non-native speakers of French progressed in deep-word knowledge when the results of third- and fourth-year students were compared with those of first-year students. Although the test contained a considerable number of French-English cognates, the L1 English learners did not perform better than the Dutch learners. The words tested were not noticeably more difficult when chosen from the 10,000-word level rather than from the 5,000-word level.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".