Analyzing feature consistency using dissimilarity matrices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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 it