Introduction to the Special Issue—Life Science in Politics: Methodological Innovations and Political Issues
Bibliographic record
Abstract
Abstract We introduce the Special Issue on Life Science in Politics: Methodological Innovations and Political Issues. This issue of Politics and the Life Sciences is focused on the use of life science theory and methods to study political phenomena and the exploration of the intersection of science and political attitudes. This issue is the third in a series of special issues funded by the Association for Politics and the Life Sciences that adheres to the Open Science Framework for registered reports. Pre-analysis plans are peer reviewed and given in-principle acceptance before data are collected and/or analyzed, and the articles are published contingent upon the preregistration of the study being followed as proposed. We note various interpretations and challenges associated with studying the science of politics and discuss the contributions.
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 | 0.016 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.011 | 0.024 |
| Insufficient payload (model declined to judge) | 0.042 | 0.017 |
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".