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
The HLVC project applies consistent methods of data collection, analysis, and interpretation to a range of languages and dependent variables. This is meant to mitigate the pattern of diverse findings from diverse studies that may partially result from diverse methods. This chapter therefore describes how the corpus is constructed, focusing on the cross-linguistic, cross-generational, and multi-method design, and gives details about recruiting, recording, and transcription of the sociolinguistic interview, the ethnic orientation questionnaire, the picture description task, and the consent procedure. It then describes the workflow for data processing and metadata construction, describing both how the corpus is organized (to be useful to additional researchers) and how we have analyzed variation of a number of variables to date. These include prodrop, case-marking, VOT, and (r) across multiple languages, apocope and differential object marking in Italian, and tone mergers, classifiers, motion-even marking, denasalization (an element of so-called lazy pronunciation, 懶音 laan5 jam1 ), and vowel space in Cantonese. It details the methods of analyzing ethnic orientation and several proxies for fluency (speech rate, vocabulary size, language-switching measures). Finally, it describes the methods used for constructing and comparing mixed-effects models for cross-variety comparisons in order to distinguish contact-induced change, internal change, and identity-marking variation.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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