Bibliographic record
Abstract
The 2012 APPEA Stand Together for Safety video used a particularly powerful message:Speak upAct mindfullyFollow the rulesGet engaged This aligned with Esso’s global safety learning focus and so it has used the SAFE theme to increase its own workforce engagement. The aim of this initiative was to build and refresh skills and knowledge in the leadership and execution of personal safety expectations using our existing baseline safety tools. A planning workshop was conducted and a small cross-functional team was established to develop the SAFE theme quarterly schedule and materials. The quarterly SAFE theme builds on safety leadership behaviours through the use of existing tools:First quarter: speak up—focuses on tools for intervention such as approaching others.Second quarter: act mindfully—hazard identification including StepBack 5 × 5 and job safety analysis and risk tolerance such as 10 factors influencing risk tolerance.Third quarter: follow the rules—life saving actions including nine procedural focus areas that save lives.Fourth quarter: get engaged—in-field review such as peer-to-peer observation. Key to the success of the initiative was the significant involvement of senior leadership and first line supervisors. This improved workforce accountability through the application, demonstration, and promotion of safety leadership values. The initiative has resulted in an improvement in our safety performance, and improved communication and alignment across our facilities.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.260 | 0.060 |
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".