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
Automated acoustical analysis is often difficult because a priori knowledge about vocalizers informs the search parameters of algorithms. As male and female voices differ on average, when measuring the voice, these a priori features unfortunately often manifest themselves by requiring the researcher to ask potentially invasive questions about the gender of the vocalizer, or assume the gender of the vocalizer in order to adjust analysis parameters. When adjusted by hand, this creates a research bottleneck. Furthermore, adjusted analysis parameters are rarely reported. With an increasing focus on inclusivity in research, as well as a focus on larger samples and big data, making new methods in voice analysis that can break these barriers accessible are essential. VoiceLab software offers automated acoustical analysis that does not require a priori knowledge of acoustic phonetics, idiosyncratic programming languages, or vocalizers’ gender, and automatically logs all analysis parameters. VoiceLab analyses are fully reproducible and require little to no knowledge about acoustical analysis from the user.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| 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 it