Paradox Theory: Proposing a Conceptual Framework for Theory Testing
Bibliographic record
Abstract
Researchers are increasingly using paradox theory. Most research on paradox theory so far has been qualitative in nature, generating novel theoretical insights and making a major contribution to the development of paradox theory. As a result, paradox theory has moved from a label and a lens to a theory and a meta-theory (Sparr, Miron-Spektor, Lewis, & Smith, 2023). However, there is a conspicuous lack of research testing the novel theoretical insights generated through qualitative research. In this paper, we critically evaluate qualitative research on paradox theory and propose a framework to accumulate and integrate some of the theoretical insights into a conceptual framework that may facilitate their testing in the future. We review the current state of testing paradox theory in quantitative studies and propose a way forward to advance it to generate a rigorous and relevant theory and practice.
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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 | 0.122 | 0.120 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.008 | 0.078 |
| Scholarly communication | 0.020 | 0.040 |
| Open science | 0.012 | 0.014 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".