Place Your Bets: Will Machine Learning Outgrow Human Labeling?
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
The AI Bookie column documents highlights from AI Bets, an online forum for the creation of adjudicatable predictions and bets about the future of artificial intelligence. Although it is easy to make a prediction about the future, this forum was created to help researchers craft predictions whose accuracy can be clearly and unambiguously judged when a prediction comes due. The bets will be documented online and regularly in this publication in The AI Bookie. We encourage bets that are rigorously and scientifically argued. We discourage bets that are too general to be evaluated or too specific to an institution or individual. The goal is not to continue to feed the media frenzy and pundit predictions about artificial intelligence, but rather to curate and promote bets whose outcomes will provide useful feedback to the scientific community. Place your bets! Please go to ai.sciencebets.org .
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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