Theorizing as Mode of Engagement in and through Extreme Contexts Research
Bibliographic record
Abstract
We explore how management and organization scholars theorize when undertaking research on extreme contexts, which are organizational settings where potential adverse events arise from risks, emergencies and disruptions. We propose that different ‘modes of engagement’ arise as researchers connect different aspects of the self to the extreme context; namely, personal self, professional self, moral self and vulnerable self. Each self-context connection plays out in different modes of engagement in the conduct of empirical research and enables different theorizing practices. We present these self-context connections as four ideal-typical modes of engagement. Adventuresome inquiry connects a personal self to the extreme context and theorizes by phenomenon-driven problematization. Instrumental scholarship expresses a professional self in the extreme context and theorizes by theory elaboration. Ideological improvement galvanizes a moral self in the extreme context and theorizes by change-driven abstraction. Reflexive labor exposes a vulnerable self and theorizes by dialectical interrogation. Our comprehensive framework of theorizing as mode of engagement contributes to extreme context research by elucidating how theorizing in and through such contexts is accomplished by researchers with multiple selves and by offering some guidance on how the four modes can be used dynamically to ensure generative theorizing. We also contribute to the broader literature on theorizing in management and organization studies by highlighting the need to consider the interplay between the researcher and the academic contributions they produce and by proposing a reflexive and dynamic framework of theorizing as modes of engagement.
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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 | 0.024 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.067 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".