Are safety and operational effectiveness contradictory requirements: The roles of routines and relational coordination
Bibliographic record
Abstract
Abstract The relationship between managing a production system to be safe and managing it to be operationally effective is often described in conflicting terms, creating confusion for research and practice. Some view improving safety as separate and distinct from increasing operational effectiveness; they are contradictory requirements. Others emphasize that safety and effectiveness are complementary, and combine to enhance competitiveness. Recent research proposes that this confusion can be explained by examining the operational and safety routines used in production. Specifically, when an organization chooses to manage safety and operations in a coordinated fashion using a joint management system, safety and operational effectiveness are complementary. Yet, the contradiction between safety and operations can occur when the functions are managed as separate and unequal silos. This research tests this supposition using the theory of relational coordination. The results, based on a combination of survey and archival safety data from 198 manufacturing firms, show that safety and operational outcomes are indirectly related via routines and that plants that manage safety and operations using a joint management system make these priorities complementary and do not create trade‐offs between safety and operational performance.
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 | 0.015 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".