Welcome to Jupyter: Improving Collaboration and Reproduction in Psychological Research by Using a Notebook System
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
The reproduction of findings from psychological research has been proven difficult. Abstract description of the data analysis steps performed by researchers is one of the main reasons why reproducing or even understanding published findings is so difficult. With the introduction of Jupyter, a new tool for the organization of both static and dynamic information became available. The software allows blending explanatory content like written text or images with code for preprocessing and analyzing scientific data. Thus, Jupyter helps documenting the whole research process from ideation over data analysis to the interpretation of results. This fosters both collaboration and scientific quality by helping researchers to organize their work. This tutorial is an introduction to Jupyter. It explains how to setup and use the notebook system. While introducing its key features, the advantages of using Jupyter notebooks for psychological research become obvious.
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 | 0.046 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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