SPADE -- Speech Across Dialects of English
Bibliographic record
Abstract
© 2020 J. Stuart-Smith, M. Sonderegger, and J. Mielke. This is an open-access OSF project which is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License. This permits you to share — copy and redistribute the material in any medium or format, and/or adapt — remix, transform, and build upon the material, provided that you do not use the material for commercial purposes, and that you give appropriate credit to the copyright owners, provide a link to the license, and indicate if changes were made. No use, distribution or reproduction is permitted which does not comply with these terms. (https://creativecommons.org/licenses/by-nc/4.0/). The Universities of Glasgow, McGill and North Carolina make no warranty whatsoever in relation to these materials including as to accuracy, quality or fitness for any particular purpose, and accept no liability in relation to the use of the materials or anything associated with such use.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".