Analyzing feature consistency using dissimilarity matrices
Bibliographic record
Abstract
Abstract In this note, we present three methods to discover the most consistent features in the World Atlas of Languages Structures (WALS). These methods measure the fit between each individual WALS feature and the overall dataset of all features combined. Features that show a strong fit to the overall dataset are hypothesised to be more central for the structure of human language than those features that show a weak fit. The three techniques we will use are based on (i) Mantel′s congruence test (MANTEL 1967), (ii) the evaluation of feature coherence relative to the overall dataset, and (iii) the comparisons of ranks. All three methods attempt to identify those features that fit best to the dataset in its entirety, though it turns out that they do not identify exactly the same features. Still, we are able to give some indications of the kind of features that appear to be most promising for future research. Finally, we investigate whether such highly consistent features might be suitable to uncover genealogical relationships between languages.
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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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".