Data from systematic audit for paper: Insights into the quantification and reporting of model-related uncertainty across different disciplines
Bibliographic record
Abstract
This upload contains 7 data files (each contains cleaned and compiled data for a given scientific field) and 2 R scripts. These files support the paper: Insights into the quantification and reporting of model-related uncertainty across different disciplines. Description of the data Compiled data files for each field contain all reviewers audit answers for eligible papers. All papers that met exclusion criteria have been removed. Data checks have been performed and formatting errors corrected either in R or manually, following steps detailed in the STAR methods. Column names and description: Number: number of question from 1 to 9 Questions: question text – question to be answered by the reviewer QuestionCode: shortened code for each question Paper: paper code - first author surname/initial and surname and year Initials: initials of reviewer Answer: answer to the question Details: extra details to support the answer Location: where in the text the uncertainty was presented Presentation: how the uncertainty was presented ModelType: type of model (focal model) Comments: any other comments from the reviewer Checks: checks of whether NA or no have been included in correct places e.g. if answers to questions 1:4 are no then question 9 is NA, if question 7 is no then 8 is NA Check 1 = when Answer = No, Location is NA Check 2 = when Answer to Number 1-4, 6 or 8-9 is Yes that Details are not NA Check 3 = when Answer = No, Presentation = NA Check 4 = when Location is not NA, presentation is not NA Check 5 = if the Answer to 5 or 7 is "No" then Answer to 6 and 8 = "NA" Check 6 = if Answer for 1-4 is "No", then Answer for 9 = "NA" Code description Two scripts are included, the first is theme_script.R, this includes code to set up a ggplot theme for the figures. The second is Figure_code.R, this script contains all code to plot and save the three figures from 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 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 | 0.179 | 0.762 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.031 | 0.033 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.389 | 0.152 |
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".