Clean up Your Theory! Invest in Theoretical Clarity and Consistency for Higher-Impact Research
Bibliographic record
Abstract
This essay starts from a concern that many empirical researchers undermine their rigorous empirical work by coupling it to unclear and inconsistent theory. I suggest this is because we underestimate the difficulty of achieving theoretical clarity and consistency. I illustrate the problem in detail by cataloging common ways we violate clarity and consistency in the articulation of theoretical constructs and relationships and illustrating these violations with examples from unpublished manuscripts. In addition, I draw on the management literature on theory writing as well as on the dual-process theory of cognition and the philosophy of science to identify and unpack three challenges to clear and consistent theory: the taxing cognitive effort required to turn ambiguous, associative intuition into logical arguments; the impossibility of achieving perfect clarity; and the existence of trade-offs between clarity and other valued qualities of theory, particularly generalizability. The implication is that researchers need to invest not just in empirical rigor but also, in theoretical rigor. Funding: The author’s research is supported in part by the Social Sciences and Humanities Research Council of Canada.
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 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 | 0.224 | 0.425 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.008 | 0.061 |
| Scholarly communication | 0.030 | 0.056 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.009 | 0.026 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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; the direct Gemma label and the distilled Codex classifier 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".