Bibliographic record
Abstract
The English comparative -er is a particular challenge for contemporary morphological analysis. The comparative and superlative in English are in an ABB suppletion relationship, which strongly suggests a containment relationship. This in turn suggests that -er and -est are in competition with each other. This is a challenge for both morphemic and word-based models of morphology. Word-based models are particularly challenged by competition between morphological and periphrastic exponence. Morphemic models, like LRFG (the model assumed here), have to deal with complex constraints on the affixal form. More and -er are in (mostly) complementary distribution, suggesting that they are allomorphs. The blocking of -er is not only triggered by phonology, but also by syntactic triggers and semantic triggers. Sometimes pure complementarity fails and both more and -er are licit (I am even madder and I am even more mad), but it does so in predictable ways (in contrast to true optionality). The net of all these properties is that the appearance of -er is the result of a complex competition involving two competitors (more and -er) and phonological, semantic, and syntactic conditions restricting their distributions.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".