Promises and perils of experimentation: The mutual internal validity problem
Bibliographic record
Abstract
Researchers run experiments to test theories, search for and document phenomena, develop theories, or advise policymakers. When testing theories, experiments must be internally valid but do not have to be externally valid. However, when experiments are used to search for and document phenomena, develop theories, or advise policymakers, external validity matters. Conflating these goals and failing to recognize their tensions with validity concerns can lead to problems with theorizing. Experimenters in psychology should be aware of the mutual-internal-validity problem, long recognized by experimental economists. When phenomena elicited by experiments are used to develop theories that, in turn, influence the design of theory-testing experiments, experiments and theories can become wedded to each other and lose touch with reality. They capture and explain phenomena within but not beyond the laboratory. We highlight how triangulation can address validity problems by helping experiments and theories make contact with ideas from other disciplines and the real world.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".