Seeing the Forest and the Trees: How a Systems Perspective Informs Paradox Research
Bibliographic record
Abstract
Abstract Paradox theory has fundamentally changed how researchers think about organizational tensions by emphasizing their oppositions and their interdependencies. Yet, most paradox studies focus on salient, perceived tensions, ignoring latent, nested tensions and their complex interconnections. This partial view is rooted in the paradox literature focusing on the epistemological realm (actors’ perception of tensions) while disregarding the ontological realm (tensions’ underlying reality). The focus on the epistemological aspects of the tensions impedes researchers from moving to more intricate insights into paradox, which could help address the realities of complex issues, such as wicked problems. We propose a systems perspective on paradox that discriminates the epistemological understandings from the ontological realities of tensions. By revealing the ontology of tensions, the underlying complexity becomes empirically interpretable. We illustrate the power of this perspective by offering two research strategies that can help researchers and organizations apprehend paradoxes grounded in systems.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.020 | 0.037 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".