Creating a Large-Scale Audio-Aligned Parsed Corpus of Bilingual Russian Child and Child-Directed Speech (BiRCh): Challenges, Solutions, and Implications for Research
Bibliographic record
Abstract
ABSTRACT The BiRCh Project (The Corpus of Bilingual Russian Child Speech) involves collecting a longitudinal audio corpus of Russian spoken by children and their families in Russia, Ukraine, Germany, the U.S., and Canada. We are building a large-scale corpus based on a subset of this data, the “Parsed and Audio-aligned Corpus of Bilingual Russian Child and Child-directed Speech (BiRCh)” with two basic components: (1) 1-million-word transcripts which are time-aligned with the audio speech signal and fully textsearchable, and (2) a 500K-word morphologically annotated and parsed portion of the transcripts, also audio-aligned. We are using this corpus to investigate various phenomena in the linguistic input and the developmental trajectory of heritage bilinguals, e.g., case, gender, passives, impersonals, politeness markers, disfluencies, and discourse markers. This article focuses on the challenges and solutions of the BiRCh development and the implications for research on the richly annotated data provided by the corpus.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".