Cognitive diversity and the future of crises: an analysis of the topic space of the biological sciences
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
This paper proposes to address the relationship between cognitive diversity and research topics among biologists. It asks whether biologists who are ‘open’ to a greater variety of topics are also more prompt to tackle issues relative to current global crises, or if some key topics like climate change, biodiversity and global health are confined to rather institutionally hermetic disciplinary landscapes. To answer this question, we propose to map a space of topics as a combination of latent topic modeling and multiple correspondence analysis. Such a method allows us to relate topics with proprieties of both journals and authors. It also provides an empirically informed framework to operationalize the cognitive diversity of biologists with reference to the distribution of their most prevalent vocabulary the space of topics. Sample for analysis is based on all publications (34,797) from all professors of biology in Switzerland between 2008 and 2020 (n=465).
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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