Bibliographic record
Abstract
Dave Donaldson integra una lista muy selecta. Este joven profesor del Instituto Tecnologico de Massachusetts (MIT) gano la Medalla John Bates Clark de la Asociacion Estadounidense de Economia al mejor economista menor de 40 anos. Paul Samuelson, Milton Friedman, Robert Solow, James Tobin, Kenneth Arrow, Gary Becker, Joseph Stiglitz y Paul Krugman, entre otros Premios Nobel, recibieron el mismo galardon en sus primeros anos de vida profesional. Donaldson es un especialista en comercio internacional, y fue uno de los oradores de la conferencia Economics of Artificial Intelligence, que reunio en Toronto en 2017 a grandes expertos internacionales para repensar el futuro de la economia de los algoritmos. En esta entrevista analiza el impacto de la inteligencia artificial (IA) en las politicas comerciales.
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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.051 | 0.013 |
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".