Instrumented Digital and Paper Reading (dataset)
Bibliographic record
Abstract
THE PRESENT DATASET CONTAINS THREE FOLDERS: i. DEMOGRAPHIC AND ANNOTATION DATASET: this .xlsx spreadsheet contains the relevant demographic data for all 25 participants in our study. Also, it includes the participant evaluation for every paragraph read during our experiment for three factors: Interest, Attentiveness and Effort. Those values are important to correlate with the Flesch–Kincaid readability score. Please refer to the paper for more details. ii. EXPERIMENT DATASET: the folder contains all the data collected during our experiments. There were 25 participants, each one with the data obtained during 16 readings in our tests, organised in consecutive folders. Each folder contains the data generated through the readings of two apparatus used in our study, an Eye-tracking and an EEG helmet. Video feed has not been included as the dataset is anonymised. iii. OPENFACE DATASET: this folder includes, amongst other data, the HOG files obtained through the processing of the participant's videos, (not included) using OpenFace, a Python and Torch implementation of face recognition with deep neural networks (https://cmusatyalab.github.io/openface/#openface). The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.067 | 0.149 |
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".