Developing a cross-platform federated code repository for speech research
Bibliographic record
Abstract
The increasing proliferation of code, scripts, and programming language libraries for use in speech science research raises concerns over the maintenance and organization of this disparate code base. These concerns have motivated us to explore the development a federated code repository under the umbrella of the Acoustical Society of America. Our primary goal for this repository is to serve as a central point from which researchers in the speech sciences can access code and data from the community that adheres to a set of established standards for documentation. The code will also be reviewed by other researchers for errors, vulnerabilities, and algorithmic misspecifications. Further, this repository will serve as a starting point for the future development of parallel libraries in open-source languages like R, Python, and Julia. In this talk, we will outline our plans for the repository and identify key open challenges and questions to be answered by contributors regarding repository structure, the code submission and review process, and documentation standards to adopt.
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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 | 0.115 | 0.255 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.009 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.015 | 0.021 |
| Open science | 0.011 | 0.020 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.031 |
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".