Clean up Your Theory! Invest in Theoretical Clarity and Consistency for Higher-Impact Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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in the articulation of theoretical constructs and relationships and illustrating these violations with examples from unpublished manuscripts.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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it