Bibliographic record
Abstract
Abstract One methodological approach to grasping a ‘big-picture’ history of modern science involves tracing the complex entanglements between scientific knowledge and the development of racism and racialized economic systems. Indeed, no historical account of any scientific field can be complete without acknowledging the role of race as an intellectual, social or economic factor. We substantiate this argument through a synthetic review of three overlapping threads in the historiography of science. First, historical research on ‘race science’ has analysed the formation of disciplines directly involved in constructing scientific concepts of race, including medicine, anthropology, linguistics, phrenology, psychology, archaeology and genetics. Second, historians have demonstrated that connections between race and science are not limited to the domain of race science. Rather, European imperial expansion, colonialism and capitalism created the foundational infrastructures undergirding the emergence of modern professional science. Finally, new research shows how race remains covertly embedded in theoretical frameworks, statistical formulae and technological devices still used by scientists today. Through these examples, we perceive a big-picture history of science in which its co-constitution with race links localized case studies and imperial narratives across space and time.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.069 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".