Attentional Triangulation: Learning from Unexpected Rare Crises
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
Attention to weak cues lies in the eyes of the beholder, but there are ways to entice such cues into collective view. To examine the link between attention to weak cues and learning from rare events, I use longitudinal, qualitative data to develop an attention-based perspective on how organizations learn from a crisis, a specific type of rare event. Learning from a crisis involves understanding why the crisis occurred and developing organizational designs for preventing the crisis from reoccurring. My data illustrate how disparity in attention to issues across the chain of command and the inability to coherently attend to weak signs of danger resulted in an unexpected crisis at Novo Nordisk, a world leader in diabetes care. The main contribution of my study is the development of the concept of attentional triangulation, which refers to the intersection of three interdependent dimensions of organizational attention (stability, vividness, and coherence) to identify issues that have the potential of having critical consequences for the organization. I also elaborate on the structures and processes that organizations can enact to facilitate attention triangulation for learning from rare events.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it