Sign Language Phonetic Annotation meets Phonological CorpusTools: Towards a sign language toolset for phonetic notation and phonological analysis
Bibliographic record
Abstract
The field of sign language linguistics still misses a unified notation system such as IPA for spoken languages. Some previous attempts to create written notation systems are either not suited for phonetic analysis, or language-specific and phoneme-based and thus impossible to use in cross-linguistic studies. We describe a more recent attempt to create a purely phonetic notation system, Sign Language Phonetic Annotation (SLPA) by Johnson and Liddell (2010, 2011a, 2011b, 2012). SLPA aims for narrow phonetic notation, is easily learned by humans and machine-readable, utilizes symbols found on a common keyboard, and does not require the user to be familiar with sign languages. However, SLPA is too exhaustive (a single handshape requires 23-34 characters), incorporates some theoretical assumptions (e.g., binary features), and captures as distinctive handshapes that anatomically impossible, redundant, or perceptually nondistinctive. We propose modifications to SLPA that make it easier to use and avoid coding errors, more user-friendly, and more linguistically relevant, both general modifications suitable for manual notation and software-specific modifications. We also discuss how we intend to adapt SLPA into the Phonological CorpusTools software (Hall et al. 2015), a free tool that allows researchers to make fast, replicable analyses of various phonological patterns.
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 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 | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.032 | 0.029 |
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".