An information-theoretic view on language complexity and register variation: Compressing naturalistic corpus data
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 This article utilises an innovative, information-theoretic metric to assess complexity variation across written and spoken registers of British English. This is novel because previous research on language complexity mainly analysed complexity variation in typological data, single language case studies or geographical varieties of the same language. The measure boils down to Kolmogorov complexity which can be conveniently approximated with off-the-shelf compression programs. Essentially, text samples that can be compressed more efficiently count as linguistically simple. The dataset covers a wide range of traditional written and spoken registers (e.g. broadsheet newspapers, courtroom debate or face-to-face conversation), as sampled in the British National Corpus . It turns out that Kolmogorov-based register variation coincides with register formality such that informal registers are overall and morphologically less complex than more formal registers, but more complex in regard to syntax (defined here as rigid word order). Generally, the results show that written and spoken registers vary along a continuum, and significantly trade-off morphological against syntactic complexity (and vice versa). Finally, the findings support proposals to view language as a complex adaptive system and demonstrate how language adapts to the situational context of language production and functional-communicative needs of its users.
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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 | 0.001 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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