Bibliographic record
Abstract
By 2000, the neural network fever from the 1980s had broken, and neural networks became normal science again. Thomas Kuhn once characterized the time between scientific revolutions as the normal work of scientists theorizing, observing, and experimenting within a settled paradigm or explanatory framework. 1 Geoffrey Hinton moved to the University of Toronto in 1987 and continued with a steady stream of incremental improvements, although none of them had the magic that the Boltzmann machine once held for us. Hinton became the leader of the Neural Computation and Adaptive Perception (NCAP) Program at the Canadian Institute for Advanced Research (CIFAR) in the first decade of the new century, which consisted of around twenty-five researchers from Canada and other countries who were focused on solving difficult problems with machine learning. I was a member of the NCAP Advisory Board, chaired by Yann LeCun, and attended the program's annual meetings just before the NIPS conferences. Making slow but steady progress, the neural network pioneers explored many new strategies for machine learning. Although their networks had many useful applications, the high expectations for the field in the 1980s had not been fulfilled. This did not deter the pioneers from keeping the faith, however. In retrospect, they were setting the stage for a dramatic breakthrough.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.018 |
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".