Lifting the curtain on the Wizard of Oz: Biased voice-based impressions of speaker size.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The consistent, but often wrong, impressions people form of the size of unseen speakers are not random but rather point to a consistent misattribution bias, one that the advertising, broadcasting, and entertainment industries also routinely exploit. The authors report 3 experiments examining the perceptual basis of this bias. The results indicate that, under controlled experimental conditions, listeners can make relative size distinctions between male speakers using reliable cues carried in voice formant frequencies (resonant frequencies, or timbre) but that this ability can be perturbed by discordant voice fundamental frequency (F-sub-0, or pitch) differences between speakers. The authors introduce 3 accounts for the perceptual pull that voice F-sub-0 can exert on our routine (mis)attributions of speaker size and consider the role that voice F-sub-0 plays in additional voice-based attributions that may or may not be reliable but that have clear size connotations.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 it