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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".