Using Computational Modeling for Building Theory: A Double Edged Sword
Bibliographic record
Abstract
Computational modeling is a powerful method for building theory. However, to construct a computational model, researchers need to operationalize their cognitive or verbal theory into the specific terms demanded by the simulation's language. This requires the researcher to make a series of reasonable assumptions to fill unanticipated "specificity gaps." The problem is that many other reasonable assumptions could also have been made, and many of those resulting models would also match the conceptual theory. This is the problem of equifinality. We demonstrate the power and the dangers of computational modeling by building a simulation of a classic small group study. The results demonstrate that reasonable assumptions and equifinality are straightforward (but o en overlooked) problems at the core of genuinely useful methodology. We o er recommendations and hope to open a dialog on other perspectives and solutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.063 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.062 |
| Scholarly communication | 0.019 | 0.042 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".