Bibliographic record
Abstract
Purpose In recent years, organization scholars have engaged in several conversations about the process of theory development, and offered many proposals for building new theories of organization. The purpose of this paper is to highlight a fundamental, fruitful and often neglected method for developing new theories of organization. Design/methodology/approach This paper draws on Peirce's typology of reasoning: deduction, induction and abduction. This typology helps in analyzing and categorizing the extant proposals for developing new theories of organization, and also makes it visible what approach has been most often missing. Findings This paper shows that the offered proposals can be categorized into the following two models: (1) armchair theorizing; (2) present capturing. This categorization also highlights a third model – change sensitizing – that is based on shifting organization theories by sensitizing ourselves to macro shifts of organizational reality. Originality/value Although the change sensitizing model is an unusual, marginal practice in today's organization research, it has historically been used to develop many of the renowned theories in social sciences. If taken as a serious agenda, it has the potential to generate a host of new, valuable theories of organization.
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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 | 0.041 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.009 | 0.128 |
| Scholarly communication | 0.026 | 0.043 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.020 |
| 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".