Why and how do theory groups get ahead in organization studies? Groundwork for a model of discursive moves
Bibliographic record
Abstract
This article puts forth a model of academic discourse as a set of discursive moves occurring in a three-dimensional space (substantive, methodological and conceptual). The model is used to make sense of the dynamics of the intellectual landscape of organizational research, and to answer the question: When different theory groups (groups of scholars sharing a common set of discursive commitments) vie to explain a particular phenomenon or solve a problem relevant to the study of organizations, which — if any — is likely to become dominant in the literature and why? The article applies the model primarily to competitive interactions among certain types of theory groups. However, the article also shows how the model can be applied to understand both competitive and non-competitive interactions among theory groups, and both teleological and communicative intentions on the part of the movers.
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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 | 0.060 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.013 | 0.092 |
| Scholarly communication | 0.026 | 0.052 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".