Bibliographic record
Abstract
New ideas no longer fuel economic growth the way they once did. A popular explanation for stagnation is that good ideas are harder to find, rendering slowdown inevitable. We present a simple model of the lifecycle of scientific ideas that points to changes in scientist incentives as the cause of scientific stagnation. Over the last five decades, citations have become the dominant way to evaluate scientific contributions and scientists. This emphasis on citations in the measurement of scientific productivity shifted scientist rewards and behavior on the margin toward incremental science and away from exploratory projects that are more likely to fail, but which are the fuel for future breakthroughs. As attention given to new ideas decreased, science stagnated. We also explore ways to broaden how scientific productivity is measured and rewarded, involving both academic search engines such as Google Scholar measuring which contributions explore newer ideas and university administrators and funding agencies utilizing these new metrics in research evaluation. We demonstrate empirically that measures of novelty are correlated with but distinct from measures of scientific impact, which suggests that if also novelty metrics were utilized in scientist evaluation, scientists might pursue more innovative, riskier, projects.
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How this classification was reachedexpand
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 | 0.064 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.060 | 0.030 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".