Дюденева рать и политическое развитие тверской земли
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 existence of the Language Familiarity Effect (LFE), where talkers of a familiar language are easier to identify than talkers of an unfamiliar language, is well-documented and uncontroversial. However, a closely related phenomenon known as the Other Accent Effect (OAE), where accented talkers are more difficult to recognize, is less well understood. There are several possible explanations for why the OAE exists, but to date, little data exist to adjudicate differences between them. Here, we begin to address this issue by directly comparing listeners' recognition of talkers who speak in different types of accents, and by examining both the LFE and OAE in the same set of listeners. Specifically, Canadian English listeners were tested on their ability to recognize talkers within four types of voice line-ups: Canadian English talkers, Australian English talkers, Mandarin-accented English talkers, and Mandarin talkers. We predicted that the OAE would be present for talkers of Mandarin-accented English but not for talkers of Australian English-which is precisely what we observed. We also observed a disconnect between listeners' confidence and performance across different types of accents; that is, listeners performed equally poorly with Mandarin and Mandarin-accented talkers, but they were more confident with their performance with the latter group of talkers. The present findings set the stage for further investigation into the nature of the OAE by exploring a range of potential explanations for the effect, and introducing important implications for forensic scientists' evaluation of ear witness testimony.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.011 |
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