Testing the malleability of teachers’ judgments of second language speech
Bibliographic record
Abstract
Abstract This study examined whether a negative social bias can influence how teachers evaluate second language (L2) speech. Twenty-eight teachers of L2 German from Western Canada – 14 native speakers (NSs) and 14 proficient non-native speakers (NNSs) – rated recordings of 24 adult L2 learners of German across five speech dimensions (accentedness, comprehensibility, vowel/consonant accuracy, intonation, flow) using 1,000-point scales. Immediately before rating, half of NS and NNS teachers heard critical comments about undergraduate German students’ language skills, while the other half heard no biasing comments. Under negative bias, while the NNS teachers provided favorable evaluations across all five measures, NS teachers followed suit for only intonation and flow, downgrading L2 speakers’ accentedness, comprehensibility, and vowel/consonant accuracy. Findings call into question the relative stability of L2 speech ratings and highlight the importance of social context and teacher status as native versus non-native speakers of the target language in assessments of L2 speaking performance.
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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 | 0.031 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".