Icy Targets in Karajá ATR Harmony as Contrast Preservation
Bibliographic record
Abstract
This paper presents a novel application of Contrast Preservation (Lubowicz 2003) to analyze a puzzling pattern of icy targets (Jurgec 2011). Icy targets are segments which harmonize but then block the spread of harmony, and a particularly theoretically challenging type is present in Karajá, in which derived and underlying [+ATR] high vowels behave differently (Ribeiro 2002;2012). We show that this harmony pattern can be successfully analyzed by considering the behaviour of these icy targets as a form of contrast preservation, where high [-ATR] vowels must harmonize when followed by a [+ATR] vowel, but the underlying contrast between [-ATR] and [+ATR] high vowels is preserved on any preceding vowels. The icy target effect thus emerges as a way to compromise between the pressure to harmonize high vowels and the pressure to preserve underlying ATR contrasts in high vowels. In this way, we extend Contrast Preservation Theory to include vowel harmony patterns, opening new opportunities to analyze puzzling patterns as a choice in which contrasts to preserve.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".