Language as a Stack of Homeostatic Property-Cluster Kinds: From Phonemes to Constructions
Bibliographic record
Abstract
This paper develops two operational diagnostics – projectibility and homeostasis – for deciding when linguistic categories warrant treatment as homeostatic property-cluster (HPC) kinds. Projectibility asks whether a category supports reliable out-of-sample inference; homeostasis asks whether identifiable mechanisms plausibly maintain the cluster over time and across instances. I apply these diagnostics to three structural levels. At the phoneme level I use PHOIBLE inventories to show family-wise concentration of inventory sizes and a scaling relation for the front-rounded vowel /y/; at the lexical level I trace diachronic distributional neighbourhoods to show that some lexemes drift while preserving sufficient cohesion for prediction; and at the constructional level I examine \textit{let alone} to show that a small bundle of cues transfers across corpora and degrades predictably under ablation. The contribution is methodological: concrete, reproducible tests that keep kind-claims local and evidence-driven. Where both diagnostics succeed, treating a category as an HPC is empirically warranted; where they fail, a more local or descriptive account is preferable.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 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; both teacher heads agree on what is shown here.
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".