The importance of rare events and other outliers in global strategy research
Bibliographic record
Abstract
Abstract Research Summary Rare events and other nonerror outliers (such as the COVID‐19 pandemic) are important phenomena in global strategy contexts. Despite their salience, however, they have hardly been studied systematically in our field (or organizational research at large). We suggest that this is due to a dominance of the Gaussian paradigm, which (often unrealistically) assumes linearity and independence of observations. Moreover, case‐based qualitative studies which offer contextualization have been underrepresented. We thus call on researchers to abolish the practice of habitually discarding outliers, reflect on nonnormal distributions, and pursue more qualitative studies. Journal editors and reviewers should widen their assumptions regarding “acceptable” papers and reflect on the requirement of contributing to big “T” theories. Finally, PhD training should juxtapose fundamental paradigms and associated implications for epistemological choices. Managerial Summary Extreme occurrences, such as organizational crises, recessions, or pandemics, are challenges most practitioners deal with and worry about. Understanding their determinants, characteristics, and dynamics allows for heightened vigilance, preparedness, and ultimately performance. Yet, much of global strategy research (and organizational research at large) has focused on “average” phenomena, based on methodologies that assume bell‐shaped distributions and independent observations. In this note, we argue that this is not a realistic way to think about most social phenomena. In fact, most are characterized by their high degree of interdependence among elements, as well as a relative commonness of “rare” events and outliers. As a result of embracing the reality of nonnormality, scholars will be able to offer more relevant guidance to practitioners.
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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 | 0.265 | 0.496 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.023 | 0.034 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".