Bibliographic record
Abstract
Race, gender, and age have traditionally been considered the three big social categories perceivers attend to. Recent research has highlighted how perceivers, listeners in this case, use the acoustic-phonetic information from spoken language to evaluate and categorize individuals. For example, studies have shown that different dialects elicit varying judgments of intelligence, friendliness and attractiveness. Such biases have implications regarding our assessment of individuals, be it in the clinic or the courtroom. While we know that our perception of speech can trigger biases, it is important to explore the opposite—whether our biases affect our perception of speech. This study aims to contribute to the knowledge base of the latter by examining the effects of listener biases on speech intelligibility. Listeners will be presented with sentences, independently controlled for clarity, produced by model talkers of different dialects and backgrounds. This auditory stimuli will be accompanied by an image or description of the talker as a method of eliciting or constructing a preconceived linguistic bias. After each sentence, participants will be asked to type out what they heard. Analyses will examine the proportion of correct words, confidence ratings, response time, and other measures exploring participants’ ratings of vocal qualities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".