The “tunnel vision” effect: Structuring of attention and use of digital technologies in Emergency Operation Centers
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
Preliminary fieldwork conducted in three Emergency Operations Centers (EOCs) in Canada and two in the United States has highlighted the relevance of a phenomenon that is affecting negatively collaborative work and shared situational awareness at EOCs. Namely, the observation that, in technologically dense EOCs, emergency management staff are affected by what emergency managers call “deep immersion” or “tunnel vision.” This phenomenon is characterized by channelized attention to individual interactions with computer-based systems, simultaneous disengagement from cooperative lines of work, and reduction in the use of alternative informational resources. Two consequences of this phenomenon are: reduced awareness of the alignment of other actors' actions with the ongoing situation, and impaired ability to anticipate individual actions that align timely and relevantly with collective ones. In this article, we provide a conceptual and methodological framework to structure the study of this phenomenon in EOCs and some preliminary findings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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