Differentiating crisis incidents: A replication study using the action systems model
Bibliographic record
Abstract
Abstract Crisis incidents are volatile situations that can pose significant risk to those involved and to law enforcement. The idiosyncratic conditions that lead to such incidents, as well as their volatility, often militate against consistent explanatory models. However, the application of overarching paradigms, such as the action systems model, has shown some promise in imposing order in the domain. Recent research has successfully differentiated crisis incidents into the four distinct modes of the action systems model: conservative, adaptive, integrative, and expressive. The purpose of this paper is to attempt to replicate this recent study using 242 cases from the United States, Ireland, Canada, and Sweden. Data analysis involves smallest space analyses and constrained multidimensional scaling. Although the results supported the underlying structure of original proposed behavioural model, there are a few deviances. These differences along with the potential influence of cultural variations, offence variable selection, the type of incident, and the sample under scrutiny are discussed. It is evident that there remain several challenges, and further research is required, prior to developing a unified framework.
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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 | 0.044 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".