An exploratory study of the hypothesis of divisible versus unitary competence in second language proficiency
Bibliographic record
Abstract
In this research Oiler's question 'Is language proficiency divisible into components?' was explored by determining which of three models best fit the experimental data: a model postulating numerous specific sources of variance (the extreme divisible model), a model postulating a single, large source of variance (the unitary model), or a model postulating a large general factor and several smaller specific factors. Following analysis of data gathered in a preliminary study, four tests which had clearly recognizable contrasts in content (grammar vs. vocabulary) and mode (listening vs. reading) were constructed to identify linguistic and method variance in a correlation matrix of language proficiency variables. These four measures were pilot tested, revised, and administered in conjunction with eight other language measures to a group of beginning-level ESL learners. The data were factor analyzed using image analysis to explore the relative congruency of the three models to the data. In addition, the relationships between the tests and the demographic variables age, sex, length of time in English Canada, and first language were also investigated. In the factor analysis, both of the methods used to determine the number of factors to be retained in the final solution indicated three. (The methods used were the Kaiser-Guttman criterion of selecting factors with eigen values greater than one in a principal components analysis and the inspection of a varimax rotation of a full image analysis to determine the first factor with negligible coefficients.) When transformed using a Harris-Kaiser oblique transformation (Independent Clusters), the data presented evidence for a grammar factor, a vocabulary factor and an age-related factor which may be linked closely to the hearing ability of the students. In addition, the analyses suggested the possibility that a listening-mode factor and what I have termed a 'speed of processing factor' were also influencing the variables. The factors, however, were highly correlated, suggesting the presence of a strong general factor underlying all of the measures. The analyses of the specific relationships between each of four demographic variables (age, sex, first language, and the length of time the subject had been in Canada) and each of the twelve language variables revealed a strong negative correlation between the language measures and two of the demographic variables, age and length of time in Canada. In addition, this set of analyses revealed that the Chinese as a group performed differently than non-Chinese as a group. The analysis of sex produced no significant findings. The conclusion of the study was that the language proficiency data in this study was best modelled by a large general factor and two specific, content-related factors, grammar and vocabulary. The possibility of specific factors related to mode was not ruled out.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".