A Bayesian evaluation of the cost of abstractness
Bibliographic record
Abstract
Abstract This chapter argues that opaque phonological analyses based on abstract elements can be justified by employing domain-general reasoning. The authors treat abstractness in phonology from a Bayesian perspective by examining opacity in Kalaallisut, an Inuit language of Greenland. All other things being equal, a Bayesian learner will favor the model with the highest probability given the data available. In this case, both abstract rule-ordering solutions and concrete surface-oriented approaches adequately account for the data. But Bayesian reasoning provides an answer as to how to weigh the trade-off between increasing the complexity of the grammar (via rule ordering) and increasing the complexity of the lexicon (via new phonemes). A Bayesian learner will prefer opaque solutions (a rule system including rule ordering) over adding new phonemes to the language, since a learner pays for a rule only once, but pays for an increase in the phoneme inventory with every lexical item.
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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 | 0.010 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".