How sociolinguistic factors shape children’s subjective impressions of teacher quality
Bibliographic record
Abstract
When university students are asked to rate their instructors, their evaluations are often influenced by the demographic characteristics of the instructor-such as the instructor's race, gender, or language background. These influences can manifest in unfair systematic biases against particular groups of teachers and hamper movements to promote diversity in higher education. When and how do these biases develop? Here, we begin to address these questions by examining children's sociolinguistic biases against teachers who speak with different accents. To do this, we presented 5-year-old Canadian English-speaking children with pairs of adult talkers. Children were asked to select "who they'd like to be their teacher" then they rated "how good of a teacher" they thought each talker would be on a 5-point scale. In each trial, one talker spoke in the locally dominant variety of Canadian English, and the other spoke in a different accent. Children strongly preferred Canadian-accented teachers over teachers who spoke with non-native (i.e., French or Dutch) accents, but also demonstrated a preference for Canadian teachers over teachers who spoke with non-local regional accents (i.e., Australian or British). In line with the binary choice data, children rated the Canadian talkers more favourably. The relationship between the gender of the teacher and the gender of the child also impacted ratings. This work demonstrates that even at the onset of formal education, children may already exhibit signs of accent-based biases. We discuss these findings in relation to the growing literature on implicit bias in higher education.
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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 | 0.008 |
| 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 | 0.001 |
| 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".