Gaining Perspective of an Industry’s Readiness for Regulatory Change: A Case Study From the Aviation Industry
Bibliographic record
Abstract
A change in regulatory policy regularly affects more than one organization and may involve an entire industry comprised of thousands of diverse organizations. These types of regulatory changes often encounter significant resistance from industry stakeholders as they often view new regulation with a certain level of skepticism, contributing to policy gridlock. A significant factor in whether any change initiative fails or succeeds is the organization’s readiness for change. However, a preponderance of the organizational change research to date has focused on individuals, targeted small groups, or single organizations – little has focused on regulatory policy changes that may affect a very large and diverse industry group. By better understanding an industry’s readiness for change, regulators may more effectively identify and understand the potential opposing forces, develop strategies to overcome these forces, and therefore may create a change vector. Recently, the Federal Aviation Administration proposed a major regulatory change affecting the United States’ aviation repair station industry. This heavily debated regulation would require industry organizations to develop a formal Safety Management Systems. Thus far the regulation has met stiff industry resistance. This research attempted to gain perspective of the industry’s readiness for change and found their overall readiness level was low. Although this research was limited in scope and intended as an initial exploration of change readiness concepts across a large industry faced with a proposed major public policy shift, the results suggest readiness for change assessments may assist public agencies with managing major regulatory change.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".