Open Theorizing in Management and Organization Studies (WITHDRAWN)
Bibliographic record
Abstract
We explore how open theorizing contributes to theory development within and across scholarly communities in management and organization studies. Open science precepts such as open data and open research material can foster open theorizing, enabling loosely coordinated researchers to develop theoretical explanations by drawing on each other's datasets, code scripts, notes, methodological protocols, auxiliary findings, and supplemental documentation. In social scientific, theory-intensive fields such as management and organization studies, open theorizing processes, however, can also take place through the sharing of framings, concepts, theoretical relations, and case examples; they can even happen via debates about values in research. By enacting the social epistemological principles of free criticism and diversity, these processes significantly affect theoretical vocabularies, promoting their concentration, extension, reinvigoration, and procreation. We examine how open theorizing can thus benefit theory development.
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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 | 0.090 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.012 | 0.079 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".