Developing and maintaining the <i>Phonological CorpusTools</i> software
Bibliographic record
Abstract
Phonological CorpusTools (http://phonologicalcorpustools.github.io/CorpusTools/) is a free, open-source, cross-platform software written in Python 3 that comes with a graphical user interface. It is designed to facilitate the analysis of phonological patterns in transcribed data, calculating characteristics such as phonotactic probability, functional load, neighbourhood density, informativity, and degree of complementary distribution. Having such a tool helps increase the reproducibility of quantitative phonological corpus analysis. We discuss some of the challenges we have encountered in developing this software (and its companion, Sign Language Phonetic Annotator & Analyzer(SLP-AA)), including (1) the difficulty of providing long-term, ongoing development and support in a world of changing technologies and (2) the difficulty of making a software tool that is easily accessible to users with a wide variety of starting data types. Having centralized, staffed, and funded repositories (cf. Alveo: http://alveo.edu.au/) would help mitigate some of the practical difficulties of sharing, distributing, and maintaining such resources. We also suggest that when developing specific tools, it is important to invest considerable time and resources in the input/output interface, which in turn facilitates both the uploading of data by disparate users and the workflow of transferring data from one application to another.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.055 | 0.040 |
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".