Self-evaluations and the language of the beholder: objective performance and language solidarity predict L2 and L1 self-evaluations in bilingual adults
Bibliographic record
Abstract
People are often asked to self-evaluate their abilities, and these evaluations may not always reflect objective reality. Here, we investigated this issue for bilingual adults' self-evaluations of language proficiency and usage. We specifically examined how people's self-reported language solidarity impacted their first- (L1) and second-language (L2) self-evaluations, while statistically controlling for their objective language performance (i.e. LexTALE). We also investigated whether this impact varied for value-laden evaluations (e.g. how "good" am I at my L2) vs. usage-based evaluations (e.g. how often do I use my L2) for two sociolinguistically distinct groups (i.e. English-L1 speakers vs. French-L1 speakers in Montreal). Starting with value-laden self-evaluations, we found that French-L1 speakers with more favourable L2-English solidarity tended to underestimate their objective L2 ability, whereas French-L1 speakers with less favourable L2-English solidarity more accurately estimated their objective L2 ability. In contrast, English-L1 speakers with more favourable L2-French solidarity more accurately estimated their objective L2 ability than those with less favourable L2-French solidarity who underestimated their L2-French abilities. Turning to usage-based self-evaluations, we found that participants' self-evaluations were generally more accurate reflections of their performance, in a manner that was less affected by individual differences in self-reported language solidarity. This implies that language solidarity (or perhaps language attitudes more generally) can implicitly or explicitly impact bilingual adults' language self-evaluations when these evaluations are value-laden. These data suggest that people's language attitudes can bias how they perceive their abilities, although self-evaluations based on language use may be less susceptible to bias than those that are value-laden. These data have implications for the study of language and cognition that depend on self-assessments of individual differences and are relevant to work on how people self-assess their abilities generally.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".