Automatic affective reactions to physical effort
Bibliographic record
Abstract
Dataset for the study titled "automatic affective responses during physical effort: a virtual reality study". This dataset includes: <strong>1) A codebook (including the name of the main variables)</strong> --> "code_book_affect_effort.xlsx" <strong>2) Behavioral data (raw)</strong> --> in the folder "data_ps_VR". The raw data are added for transparency, but are not necessary to run the models. <strong>3) Self-reported data (raw)</strong> --> "20220112_VR_expe.xlsx" --> "20220112_VR_pilot.xlsx" The raw data are added for transparency, but are not necessary to run the models. <strong>4) clean data ready to used for the statistical analyses</strong> --> "data_VR_all_clean.csv". This clean data are produced by the R script. These data included the self-reported and the behavioral measures. <strong>5) R script for the data management (i.e., from the raw data to data ready to be analyzed)</strong> --> "data_management_effort.R" to create the dataset (return the file: "data_VR_all_clean.RData") <strong>6) R script for the models tested</strong> --> "Data_mixed_effects_models.R" for the models tested in the paper
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.559 | 0.072 |
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; both teacher heads 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".