eXpLore Leadership Programme—HSSE Training Specific to the Needs of Exploration Leaders
Bibliographic record
Abstract
Abstract The design and delivery of an HSSE module on Shell's eXpLore leadership programme was a direct response to requests from exploration staff and leaders for more structured attention on HSSE in a manner relevant to the specific HSSE challenges which Shell's Exploration business faces. The objectives of the HSSE module of the Shell eXpLore programme are to: –Raise the profile of HSSE in Shell's Global Exploration Function by creating a passion and sense of ownership for HSSE;–De-mystify HSSE management and create a sense of confidence in using HSSE systems;–Demonstrate leadership commitment to HSSE;–Simplify and prioritise Shell's HSSE requirements, processes and procedures;–Create an environment in which HSSE experience is part of the development and progression of talented individuals;–Ensure that a ‘high reliability’ HSSE culture is intrinsic to our planning and execution of ventures;–Offer refreshing external insights to familiar internal HSSE challenges.–Impact the performance of our (new) ventures.Fig. 1The eXpLore leadership programme covers six different business and capability themes (shown in blue) within the exploration business process. It was launched in September 2004 to complement existing Shell leadership training programmes. The programme's goal is to enhance exploration performance by creating a well led, aligned, highly skilled and highly motivated exploration community. The new HSSE eXpLore module was designed jointly by University of Cambridge faculty, their supporting consultants and Shell's senior Exploration Leaders. The first 2.5 days are filled with a rich programme of lectures, simulations and exercises, relevant case studies, faculty from Shell, Cambridge and associated Universities, and renowned external speakers. This is followed by a unique ‘real work’ project of 1.5 days to solve urgent and real-time HSSE issues for a new Exploration venture. The course also includes a hazard spotting tour in Cambridge, which is not only great fun, but also allows networking and strengthen the global Exploration community.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.007 |
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".