L2 motivation among hearing learners of Finnish Sign Language
Bibliographic record
Abstract
The realisation of the linguistic rights of Deaf individuals is, to a considerable extent, dependent upon whether there are majority language speakers who acquire a sign language as an L2 and use their L2 skills. Still, the motivation of hearing persons in learning sign languages as L2s is a largely unmapped area. This study seeks to capture the motivation underlying the L2 use among hearing learners of Finnish Sign Language (FSL) in terms of current theorising on L2 motivation and to test the applicability of central constructs in L2 motivation research in a specific SLA context. We collected data in 2018 using an anonymous online questionnaire (N = 173). We tested a serial mediational model that linked L2 learning orientations and L2 learning experience, through a set of mediating variables, to L2 use. The model was statistically significant and explained 66% of the variance in L2 use. In addition, integrativeness significantly moderated the effect of L2 competence on L2 use. The findings from the quantitative analysis are enriched with the analysis of the respondents’ comments. We discuss the results from the perspective of how hearing learners of sign languages can extend the communication networks of Deaf sign language users.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".