Bibliographic record
Abstract
Aitor Anduaga is an Ikerbasque research professor at the Basque Museum of History of Medicine and Science, University of the Basque Country. He has been a visiting scholar at the Universities of Oxford, Sydney, Montreal and Toronto, the Max Planck Institute for the History of Science of Berlin and the Smithsonian Institution of Washington. He has published extensively on the social history of physics and technology. His main works are: Wireless and Empire. Geopolitics, Radio Industry and Ionosphere in the British Empire, 1918-1939 (Oxford University Press, 2009); and Geophysics, Realism and Industry. How Commercial Interests Shaped Geophysical Conceptions, 1900-1960 (Oxford University Press, 2016). He has also devoted to Basque studies; he is author of La cadena vasca. Educación, tecnología, poder social y rendimiento industrial, 1776-1902 (2010), a metaphor about the transformation of the Basque society from a proto-industrial condition to a fully industrial and modern one. He is also preparing a work on the Basques and the Philippines. Ideas, works and lives: a selected bio-bibliography (forthcoming). See more at http://www.ikerbasque.net/en/aitor-anduaga\nEducation: Graduated in Physics; graduated in Philosophy; Ph.D. Physics.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 0.030 |
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".