The subject of ROAR in the mind and in the corpus: What divergent results can teach us
Bibliographic record
Abstract
The use of different research methods in linguistics invariably leads to questions about the convergence and divergence of research findings. Aiming for convergence, while understandable, may distort our understanding of language phenomena, if convergence is seen as the only publishable result. We suggest a place for diverging results in furthering our understanding of the data techniques used to investigate linguistic phenomena. We illustrate this point through an experimental and corpus-based investigation of the preferred syntactic subjects of the English verb ROAR and discuss how deeper reflection on these diverging results leads to a better understanding of the different data types.
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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 | 0.198 | 0.455 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.007 | 0.030 |
| Scholarly communication | 0.024 | 0.045 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".