Organizations and Risk in Late Modernity
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
Risk is an important but under-investigated feature of organizations in Late Modernity. This paper introduces the Special Issue on Organizations and Risk in Late Modernity. The rationale for the special issue is discussed. An overview of important approaches to risk research and organizations is provided to frame the special issue. These approaches include the cognitive science approach, which takes a positivist perspective and assumes that risks are objective and knowable. This view is contrasted with socio-cultural theories based in work by Mary Douglas, Ulrich Beck, Anthony Giddens and Michel Foucault. Charles Perrow's organizational theory of the production of risk and accidents due to interactive complexity, and Karl Weick's theory of risk sensemaking, are then discussed. The paper then reviews the contributions of papers in the special issue and outlines issues for future research on risk and organizations.
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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 | 0.002 |
| 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) | 0.000 | 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