Theorizing as Mode of Engagement in and through Extreme Contexts Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it