Control familiarity bias when shifting to a risk-based approach: Lessons from the Temporary Traffic Management industry
Bibliographic record
Abstract
Risks are apparent in all industries, but what if the industry you worked in had been focussed on familiar, existing controls that didn’t eliminate risk? The Temporary Traffic Management (TTM) industry historically has had a culture that tends to accept risk, as working on or near roads carries an inherent level of risk. TTM worksites account for 66 serious and fatal injury crashes each year in New Zealand. To reduce incidents on worksites there has been a shift from prescriptive guidance to a risk-based approach, with the aim of moving to more impactful safety controls. A national TTM worker survey was created to review attitudes, reported behaviours on site, and the acceptance and adoption of a risk-based approach. Survey insights revealed that even in a higher-risk industry with a strong focus on workplace safety, engaged workers still have challenges to overcome. In the TTM context these were, correct identification of risk and appropriate controls, habituation to risk, and the pressure to balance and trade risk against competing requirements, like cost and delays to traffic. New insights indicate a Control Familiarity Bias (CFB), where controls that are available, familiar, easy and embedded are preferred, selected and assigned an overinflated weight in safety decisions, even where better alternatives exist.
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.
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 | 0.069 | 0.171 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".