Bibliographic record
Abstract
An instrument to measure attitude toward the learning of French as a second language (ALFS) was developed. Instruments already developed to measure motivational intensity and orientations (instrumental and integrative) were modified to fit the local situation. The experimental subjects were 100 students from grades 7 and 8. Twenty-five students were selected randomly from each grade and sex, out of a total of 571, receiving instruction in French by "Le Francais International ° method in a mid-western Canadian city. Each of the subjects was rated on achievement by their respective teachers on a 5-point scale. The ALFS scores intercorrelation matrix produced 4 principal factors which were rotated to varimax- and promax-criterion. These factors were interpreted to be pragmatic, possessive, perseverance, and reflective attitudes. The intercorrelation matrix of the 4 factor scores, motivational intensity, orientations, and achievement ratings, resulted in only one principal factor establishing ALFs factors as correlates of the other variables. A step-wise regression analysis revealed that perseverance factcr and motivational intensity were the most effective of the 7 competing predictors of achievement in French, accounting for 26.8 % of the variance, whereas the entire set of predictors accounted for 30.6 % of the variance. (Author)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.114 | 0.028 |
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".