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.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.203 | 0.090 |
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".