Speaker evaluations in multilingual contexts: The predictive role of language and nationality attitudes as distinct factors in explicit and implicit cognition
Bibliographic record
Abstract
Previous speaker evaluation models have assumed that, in social interactions, attitudes towards languages are the most salient cues that are used to form an evaluation of the person who is speaking (Giles & Marlow, 2011). The role of attitudes towards the speaker’s national group has not been addressed because most studies have been conducted in monolingual contexts in which spoken language serves as a clear indicator of national group membership. However, the concepts of language and nationality cannot be equated in multilingual societies, which are characterized by various nationals using different languages. The present dissertation addresses the need for the development of a revised theoretical model for multilingual contexts by making a distinction between language, nationality, and speaker concepts on both an explicit and an implicit level. The adapted model applies social-cognitive theories that propose a distinction between explicit and implicit processes and further posits a differential predictive influence of explicit and implicit attitudes on explicit and implicit speaker evaluations. In the multilingual context of Luxembourg, three successive studies were conducted by adapting an audio-based Implicit Association Test (IAT) as an implicit measure of language and nationality attitudes and an evaluative priming task as implicit measure of speaker evaluations. The findings emphasized the convergent and discriminant validity of language and nationality attitudes on both an explicit and an implicit level. Furthermore, the distinctness of explicit and implicit speaker evaluations was confirmed such that explicit evaluations were influenced by explicit attitudes, and implicit evaluations were affected by implicit attitudes. In the fourth study, the model was transferred to the linguistic context of Montreal (Canada). The findings showed that implicit speaker preferences were affected by implicit nationality attitudes affirming model transferability. Overall, the dissertation shows that language is a salient factor in explicit person perception, whereas nationality plays a vital role on an implicit level, demonstrating the added value of the language-nationality and the explicit-implicit distinction in the speaker evaluation formation. Self-reports diverged from implicit measures such that an in-group bias was visible only on an implicit level, giving insight into the effect of specific socio-contextual factors in a given linguistic context as well as the practical implications for decision makers in professional domains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".