Bibliographic record
Abstract
Archive of processed aggregated EEG, behavioral, and background information. Data are from the ONLY study, reported in the paper 'Prosodic processing in sentences with ‘only’ in L1 and L2 English'. Passive listening ERP study on processing contrastive pitch accents in British-English sentences with 'only'. Time-windows looked into expectancy (EN), P200-like 'accent positivity', and P600. Participants were adults. One group was highly proficient Dutch speakers of English. The other group were native speakers of English from the United States (n = 8), England (n = 7), Ireland (n = 1), Australia (n = 2), Canada (n = 1), and British community in Hong Kong who did not have knowledge of Mandarin Chinese or Cantonese (n = 1). Contains the folders: - stimuli: All audio files used in the experiment - acoustic analysis: Analysis performed on the prosodic information of the stimuli - eeg experiment: All files required to run the experiment, e.g. codebook, NeuroBS Presentation scripts, stimuli, logfiles. - pp info and behav data: aggregated participant and behavioral information, and scripts to process task accuracy. - eeg data: Raw EEG files, Brainvision Analyzer history files and accompanying description in PDF, and Matlab scripts for further preprocessing of trial data. - eeg analysis: Aggregated ERP long-format datasets for analysis in R, R scripts, and final model information in linear modeling.
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.458 | 0.354 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".