Benefits of Open and High-Powered Research Outweigh Costs
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
Several researchers recently outlined unacknowledged costs of open science practices, arguing these costs may outweigh benefits and stifle discovery of novel findings. We scrutinize these researchers' (1) statistical concern that heightened stringency with respect to false-positives will increase false-negatives and (2) meta-scientific concern that larger samples and executing direct replications engender opportunity costs that will decrease the rate of making novel discoveries. We argue their statistical concern is unwarranted given open science proponents recommend such practices to reduce the inflated Type I error rate from .35 down to .05 and simultaneously call for high-powered research to reduce the inflated Type II error rate. Regarding their meta-concern, we demonstrate that incurring some costs is required to increase the rate (and frequency) of making true discoveries because distinguishing true from false hypotheses requires a low Type I error rate, high statistical power, and independent direct replications. We also examine pragmatic concerns raised regarding adopting open science practices for relationship science (pre-registration, open materials, open data, direct replications, sample size); while acknowledging these concerns, we argue they are overstated given available solutions. We conclude benefits of open science practices outweigh costs for both individual researchers and the collective field in the long run, but that short term costs may exist for researchers because of the currently dysfunctional academic incentive structure. Our analysis implies our field's incentive structure needs to change whereby better alignment exists between researcher's career interests and the field's cumulative progress. We delineate recent proposals aimed at such incentive structure re-alignment.
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 | 0.028 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.007 | 0.044 |
| Research integrity | 0.000 | 0.000 |
| 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 it