Investigating the impact of <i>attitude</i> on first language attrition and second language acquisition from a Dynamic Systems Theory perspective
Bibliographic record
Abstract
The factor attitude is generally considered to be among the most influential for first language (L1) attrition. Nevertheless, empirical validations have proven difficult to establish. While some studies could not find clear links between measures of attitude and L1 attrition (Hulsen, 2000; Yağmur, 1997), others showed that attitudes generated from exceptional life events strongly influenced attrition (Schmid, 2002) and that pragmatic vs. ideological motivation to emigrate and ensuing attitudes were clearly linked to L1 attrition (Ben-Rafael & Schmid, 2007). A closer examination of these studies yields a noteworthy pattern: those studies that relied on questionnaires (Hulsen, 2000, Yağmur, 1997) seemed to find no straight correlations between attitude and L1 performance, while the studies that used interviews (Ben-Rafael & Schmid, 2007; Schmid, 2002) established a clearer link between the two. The present study explores the impact of attitude – as measured through both questionnaires and interviews – on L1 attrition and second language (L2) proficiency. The quantitative analysis revealed partially significant results, thus suggesting that the factor attitude would have a limited impact on L1 attrition. Individual qualitative analyses, on the other hand, revealed important links between attitudes and the migrants’ language proficiency profiles. The article argues for a combination of methodological approaches in the study of L1 attrition and underlines the idea that individual-level analyses are well suited to capture the non-linearity of attitude and its impact on L1 attrition. These conclusions fit well with a Dynamic Systems Theory perspective in relation to the constant flux of attitude perceptions and their unpredictable role in attrition (de Bot, Lowie & Verspoor, 2007).
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".