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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".