Falsifiability is not optional.
Bibliographic record
Abstract
Finkel, Eastwick, and Reis (2016; FER2016) argued the post-2011 methodological reform movement has focused narrowly on replicability, neglecting other essential goals of research. We agree multiple scientific goals are essential, but argue, however, a more fine-grained language, conceptualization, and approach to replication is needed to accomplish these goals. Replication is the general empirical mechanism for testing and falsifying theory. Sufficiently methodologically similar replications, also known as direct replications, test the basic existence of phenomena and ensure cumulative progress is possible a priori. In contrast, increasingly methodologically dissimilar replications, also known as conceptual replications, test the relevance of auxiliary hypotheses (e.g., manipulation and measurement issues, contextual factors) required to productively investigate validity and generalizability. Without prioritizing replicability, a field is not empirically falsifiable. We also disagree with FER2016's position that "bigger samples are generally better, but . . . that very large samples could have the downside of commandeering resources that would have been better invested in other studies" (abstract). We identify problematic assumptions involved in FER2016's modifications of our original research-economic model, and present an improved model that quantifies when (and whether) it is reasonable to worry that increasing statistical power will engender potential trade-offs. Sufficiently powering studies (i.e., >80%) maximizes both research efficiency and confidence in the literature (research quality). Given that we are in agreement with FER2016 on all key open science points, we are eager to start seeing the accelerated rate of cumulative knowledge development of social psychological phenomena such a sufficiently transparent, powered, and falsifiable approach will generate. (PsycINFO Database Record
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".