Reconciliation can lead to better application: A rejoinder to Perrow (2009)
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
In his brief commentary, Perrow raises four issues. First, he alludes to how the misuse of bureaucratic power could explain some accidents. Second, he reiterates that normal accidents occur owing to the characteristics inherent in a system, and such accidents, irrespective of whether high reliability practices are followed or not, are inevitable. Third, Perrow asserts that complexity and coupling are independent of time of operation. The time dimension’s irrelevance, he claims, ought to be apparent from his analysis of normal accidents in systems such as the air transport and chemical industry (see Perrow, 1984). Fourth, Perrow implies that High Reliability Theory (HRT) cannot explain the sub-class of accidents that Normal Accident Theory (NAT) concerns itself with. He thus makes a case for retaining NAT alongside other theories and finds little value in our reconciliation. In fact, he finds the reconciliation inappropriate because we supposedly err in implicating time. We respond to the four issues in turn.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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