« <i>Une bombe dans la discipline </i> » : l’émergence du mouvement génopolitique en science politique
Bibliographic record
Abstract
Mobilizing scientometric analyses and semi-structured interviews, this article investigates the emergence of ‘genopolitics’ and the scientific and academic stakes surrounding the study of genetic factors of political behavior. While the first paper on genopolitics was published in 2005, it was not until 2012 that we could observe the stabilization of this scientific movement. Though genopolitics is a relatively homogenous movement, it is nonetheless affected by internal struggles relating to the construction of a scientific programme that would be regarded as legitimate by all of its members, as well as by political scientists and the rest of the scientific field as a whole. Beyond these disagreements concerning the best appellation for, and main goal of, their programme, genopoliticians advocate for the emergence of a new paradigm in political science that would resolve the anomalies observed within empirical research resorting to the dominant rational choice and socio-psychological theories. Paradoxically, one consequence of this attempt at advancing political science is to threaten its epistemological independence, as illustrated by the use of methodological standards borrowed from behavior genetics. At the individual level, genopolitics provides an opportunity for political scientists to contribute to a controversial, but innovative area of research, and thus to ameliorate their position within the scientific field.
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 | 0.062 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.011 | 0.047 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".