The Neural Hype and Comparisons Against Weak Baselines
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
Recently, the machine learning community paused in a moment of self-reflection. In a widelydiscussed paper at ICLR 2018, Sculley et al. [13] wrote: "We observe that the rate of empirical advancement may not have been matched by consistent increase in the level of empirical rigor across the field as a whole." Their primary complaint is the development of a "research and publication culture that emphasizes wins" (emphasis in original), which typically means "demonstrating that a new method beats previous methods on a given task or benchmark". An apt description might be "leaderboard chasing"-and for many vision and NLP tasks, this isn't a metaphor. There are literally centralized leaderboards1 that track incremental progress, down to the fifth decimal point, some persisting over years, accumulating dozens of entries. Sculley et al. remind us that "the goal of science is not wins, but knowledge". The structure of the scientific enterprise today (pressure to publish, pace of progress, etc.) means that "winning" and "doing good science" are often not fully aligned. To wit, they cite a number of papers showing that recent advances in neural networks could very well be attributed to mundane issues like better hyperparameter optimization. Many results can't be reproduced, and some observed improvements might just be noise.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it