The Calibrated Error-Driven Ranking Algorithm as a Solution to Oscillation in Antagonistic Constraints: A Necessary Bias for Algorithmic Learning of Kihnu Estonian
Bibliographic record
Abstract
This paper investigates the learning of Kihnu Estonian, a minority dialect of Estonian (Balto-Finnic). I propose a set of constraints to account for Kihnu Estonian vowel harmony patterns, and show that they can be used to produce a restrictive grammar for Kihnu Estonian vowel harmony. With this constraint set, I model the acquisition of Kihnu Estonian vowel harmony via the application of the Gradual Learning Algorithm (Boersma and Hayes, 2001). Antagonistic constraints in the set I adopt pose obstacles to successful learning of the vowel patterns attested in the learning data. These obstacles can be circumvented via use of the update rule from the Calibrated Error-Driven Ranking Algorithm (Magri, 2012).This update rule has been argued to be detrimental to learning variation in stochastic OT. However, though it was originally proposed to address the Credit Problem (Dresher, 1999), I show that it is in fact an elegant solution to the learning problems caused by oscillating constraints when modeling acquisition of Kihnu Estonian vowel harmony.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".