Bibliographic record
Abstract
Some 160 men and women selected from public locations agreed to participate in a voice identification experiment. Participants were instructed to listen carefully to the tape-recorded voice of a perpetrator committing a simulated armed robbery of a business establishment. Two minutes later they were asked to describe the voice characteristics of the perpetrator, to recall exactly what he said, and then attempt to identify the speaker from a six-person perpetrator-present or perpetrator-absent voice line-up. Half of the participants in each line-up heard a sample of identical phrases and the other half heard phrases non-identical to those used in the robbery. Accuracy of speaker identification was significantly better than chance; however, there were no significant differences in performance on either line-up as a function of the type of voice sample employed. The confidence-accuracy of identification correlation proved to be non-significant. No significant correlations were found between accuracy of speaker identification and completeness of voice descriptions, or speaker identification and percentage accuracy of recall of actual words used by the perpetrator, or speaker identification and percentage accuracy of recall of idea units contained in the perpetrator’s monologue. It was concluded that voice lineups should be constructed of non-identical phrases rather than the identical phrases reportedly used by the perpetrator.
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 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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".