Campbell’s Law Explains the Replication Crisis: Pre-Registration Badges Are History Repeating
Bibliographic record
Abstract
-values, and multi-study designs came to be viewed as indicators of strong science, and thus goals in and of themselves. Consequently, their use became distorted in unanticipated ways (e.g., hypothesizing after results were known [HARKing], p-Hacking, misuses of researcher degrees of freedom), and fragile findings proliferated. Pre-registration mandates are positioned as an antidote. However, I argue that such efforts, perhaps best exemplified by pre-registration badges (PRBs), are history repeating: Another useful tool has been converted into an indicator of strong science and a goal in and of itself. This, too, will distort its use and harm psychological science in unanticipated ways. For example, there is already evidence that papers seeking PRBs routinely violate the rules and spirit of pre-registration. I suggest that pre-registration mandates will (a) discourage optimal scientific practice, (b) exacerbate the file drawer problem, (c) encourage pre-registering after results are known (PRARKing), and (d) create false trust in fragile findings. I conclude that multiple design features can help support replicability (e.g., adequate sample size, valid measurement, robustness checks, pre-registration), none should be canonized, replication is the only arbiter of replicability, and the most important solution is sociocultural: to foster a field that reveres and reinforces robust science-just as we once revered and reinforced flashy but fragile science.
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 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 | 0.067 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 teacher head, 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".