Bibliographic record
Abstract
Gardner’s socio-educational model of second language acquisition has received substantial attention from L2 researchers. Several scholars, however, came to question the applicability of the model into foreign language learning (FLL) situations because Gardner’s model was based on studies that have utilized samples selected from Canada, which is presumed to be a typical context of second language learning (SLL). Therefore, the present study investigated the generalizability of Gardner’s socio-educational model into FLL situations by using two samples of Korean learners of English selected from the USA (i.e., ESL sample) and Korea (i.e., EFL sample). To this end, a multi-sample Structural Equation Modeling (SEM) analysis was performed to examine the factorial similarity of Gardner’s model across the ESL and EFL sample. Results of the SEM analysis indicated that the socio-educational model may also hold for learners of L2 in FLL situations. Further multi-sample analysis identified parameters that were not invariant across two samples of Korean learners of English, hence highlighting a call to fine-tune the Attitudes/Motivation Test Battery (AMTB) for use in the context of FLL.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".