Introduction and purpose of the tutorial series Python for Research in Psychology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In the era of advanced computers and state-of-the-art software, there have been great strides in the domains of research and engineering. These advancements have been made possible through the popularity, accessibility, and integration of computer programming. This has made automating tasks, analysing results through complex statistical analysis, and the development of more advanced artificial intelligence possible. However, in psychology, computer programming is still being underutilized. A major factor contributing to this is the steep learning curve for programming that is compounded by a lack of tutorials and knowledge specifically geared towards psychology. Thus, the purpose of this series is to bridge this gap and lower the learning barrier for researchers. This series invites anyone to send their own tutorials and contribute towards building a comprehensive guide to programming for psychological research in one of the most versatile languages: Python! For submission details, please see the guidelines below.
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.
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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it