Bibliographic record
Abstract
In models of morphosyntactic feature geometry, unmarked features are often necessarily underspecified because they are absent from the geometry and, hence, unavailable for specification. At the same time, underspecification is held to be the primary source of morphological syncretism. Together, these two approaches predict that, given the opportunity, a morphological form associated with unmarked features should participate in syncretism over and against any other form in a paradigm. While this is generally true, there are exceptional cases in which the form associated with unmarked features is precisely the one that fails to participate in syncretism. I illustrate instances of this kind and argue that they provide evidence for the availability of unmarked features within the universal geometry. If such features are available, then languages must have the option of uniquely specifying them (i.e., of ‘marking the unmarked’). When this is recognized, a simple solution to these exceptional patterns is available — one that relies only on underspecification, and not on accidental homophony or rules of impoverishment.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".