Bibliographic record
Abstract
The Corpus of North American Spoken English (CoNASE) is a 1.25-billion-word corpus of geolocated automatic speech recognition (ASR) YouTube transcripts from the United States and Canada, created for the study of lexical, grammatical, and discourse-pragmatic phenomena of spoken language, including their geographical distribution, in North American English. The size of the corpus allows rare phenomena to be considered, and because the annotation includes the video IDs of transcripts, search hits can be manually inspected and video or audio data can be accessed. As the starting point of a scripting pipeline, the corpus can also be used for large-scale acoustic analyses of North American speech. The corpus was created from 301,846 ASR transcripts from 2,572 YouTube channels, corresponding to 154,041 hours of video. The size of the corpus is 1,294,885,016 word tokens. The channels sampled in the corpus are associated with local government entities such as town, city, or county boards and councils, school or utility districts, regional authorities such as provincial or territorial governments, or other governmental organizations. The transcripts are primarily of recordings of public meetings, although other genres are also present. Video transcripts have been assigned exact latitude-longitude coordinates using a geocoding script.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.044 |
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".