Thought Experiments and Philosophy in Organizational Research
Bibliographic record
Abstract
Organization theory seems to be caught between a rock and a hard place: on the one hand, there are arguments that the field is too preoccupied with theory, leaving its work abstract and practically irrelevant. On the other hand, there are arguments that the field is overly empirical and too methods-driven, which hampers the creation of ideas that resonate with constituencies beyond the organization studies community. How to resolve this apparent conundrum? In this essay we argue that neither more theorizing nor more forensic data-driven work might address the problem; rather, and perhaps surprisingly, we propose that a philosophical stance might offer a remedy. The aim of this essay is (1) to explore thought experiments as a genuine philosophical method that is designed to develop promising ideas and concepts and (2) to reflect on how such conceptual work can help shape organization theory to be conceptually more stimulating and practically more relevant. We argue that this particular kind of conceptual work has been and should continue to be one of the hallmarks of organization theory. Thus thought experiments represent a valuable methodological extension of our toolkit as they provide crucial devices triggering transformations in thought 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 | 0.128 | 0.138 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.119 |
| Scholarly communication | 0.016 | 0.023 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".