Attitudes and motivation for learning English among elementary francophone students / Yvon J. Cloutier
Bibliographic record
Abstract
Canadian society allows for group traditions and supports \na rich variety of cultures. The pluralistic nature of this \ncountry makes it beneficial for a growing number of children \nand adults to acquire a second language. Parents of school \nchildren and prospective employers see this as an important \nendeavor. Several reasons might explain this. Firstly, the \nacquisition of a second language may be perceived as increasing \nchances of securing employment. Secondly, many people want \nto retain their cultural identity. For example, many adults \nwho come from French speaking families have lost the ability \nto speak their maternal language fluently. Later, and for \ndifferent reasons, they may want their children to learn the \nlanguage that they (the parents) have lost. As a result, they \nmay place their children in schools where the indigenous culture \nand language can be cultivated. \nFor years now educational communities have witnessed \nthe expansion and growth of second language programs in \nelementary and secondary schools across the country (Ontario \nMinistry of Education, 1976; 1977). These have been strongly \nsupported by both federal and provincial governments. Evidence \nof the promotion of English/French bilingualism and biculturalism \nin Canada, particularly in Ontario, is highly visible. One \nonly has to look at the hundreds of research projects the \ngovernment has funded to investigate factors related to the \nacquisition of a second language (Ontario Ministry of Education, 1979; 1983; 1994). Furthermore, the subventions and interest \ngenerated by the government to investigate factors related \nto the acquisition of a second language has prompted several \nresearchers to study motivational characteristics of students \nin second language programs. \nThere are various reasons why students study and persist \nin studying a second language. It is the purpose of this study \nto investigate affective components, such as competence, interest \nand utility, shown to be involved in second language learning. \nHopefully, then, any contribution that this study can make \nto the understanding of attitudinal and motivational variables \nin second language acquisition will be an asset to the \neducational community.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".