Bibliographic record
Abstract
The ultimate goal of this paper is to compare speak to with talk to in four corpora. In the Movie Corpus (Movie Corpus (MC). 20, January 2022. Online https://english-corpora.org /movies/), talk to was preferable to speak to in the films of six countries (America, the UK, Canada, Australia, New Zealand, and Ireland). It is worth mentioning that in the Movie Corpus (Movie Corpus (MC). 20, January 2022. Online https://english-corpora.org /movies/), speak to (2,620 tokens) and talk to (18,667 tokens) was the most preferred types in the 2010s. In the TV Corpus (TV Corpus (TVC). 20, January 2022. Online https://english-corpora.org /tv/), talk to is preferable to speak to in six countries’ (America, the UK, Canada, Australia, New Zealand, and Ireland) TV programs. It is noteworthy that in the TV Corpus (TV Corpus (TVC). 20, January 2022. Online https://english-corpora.org /tv/), speak to (8,279 tokens) and talk to (59,703 tokens) reached a peak in the 2010s. In the BNC (British National Corpus (BNC). 20, January 2022. Online https://corpus.byu.edu/bnc), the types speak to and talk to show the same pattern in three genres, whereas they show a different pattern in four genres. That is, speak to is 42.85% the same as talk to in their ranking. Finally, the COCA (Corpus of Contemporary American English (COCA). 20, January 2022. Online https://corpus.byu.edu/coca) clearly shows that 42.85% of forty two nouns are the collocations of both speak to and talk to.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".