Female workforce participation in the Australian oil and gas industry—a global comparison
Bibliographic record
Abstract
As the Australian oil and gas industry faces a continued shortage of skilled employees, increasing the representation of women in this industry is a business imperative. Economic success and competitive advantage may depend on attracting and retaining the skills of women. Research shows that a gender-diverse workforce can also be linked to improved business performance, innovation and corporate governance. While women make up 46% of the Australian workforce and more than 50% of university graduates, present statistics show that on average 13% of workers in the Australian oil and gas industry are women. This is a lower proportion than comparable industries in Canada and Norway: women make up 21% and 19% of workers, respectively. In Norwegian oil companies, this level is as high as 30% (4). This extended abstract briefly discusses the present research about women's retention and progression within the Australian resource sector. It outlines the initiatives being undertaken by government, industry bodies and organisations to increase the representation of women in the Australian sector, and comparable industries in Norway and Canada. This extended abstract concludes with a case study about the challenges and lessons learnt in establishing a corporate initiative to increase female participation at Clough Limited. Women@Clough is a professional forum established in April 2011 to improve the attraction, retention and progression of women in the Clough workforce. Strategies and key success factors in the establishment of the program are also examined.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".