Delivering Employment Diversity on an Offshore Oil Project
Bibliographic record
Abstract
Abstract There is a growing need to ensure that local areas receive a share of the jobs and business associated with upstream petroleum industry activity. In the case of Husky Energy's White Rose oilfield project in Newfoundland and Labrador, Canada, there is a government requirement to provide employment and business opportunities not only to residents of Newfoundland and Labrador, and other Canadians, but also to women, aboriginal peoples, visible minorities and persons with disabilities. This paper describes how this requirement is being addressed by the White Rose Project Diversity Plan. The plan uses a non-prescriptive approach, allowing Husky and its main contractors to develop targets and initiatives that are appropriate to their organizations and activities, and to labour market constraints. It employs an iterative process whereby they establish annual diversity targets and monitor success in meeting them, leading to the establishment of new targets. The plan also seeks to foster a ‘diversity culture’ within project companies, and it emphasizes collaboration between Husky, its contractors and community groups, so as to access different networks and share expertise and lessons. These approaches may have application to employment and business diversity requirements on other projects worldwide.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".