Leading transformations – a holistic approach to leadership development
Bibliographic record
Abstract
So, what was Woodside’s challenge? How does an organisation lead through a business transformation, an industry transformation and a global energy transformation simultaneously? And what was Woodside’s answer? To redefine a whole of business approach to leadership and uplift leadership capabilities. The first step was to create a framework to revolutionise the concept of leadership and uplift leadership capability. Woodside wanted every employee to identify as a leader and lead, no matter what their role. In 2021, Woodside partnered and co-created a leadership development program with the Australian Graduate School of Management (AGSM) at UNSW. The program was designed for employees to uplift their leadership capability while empowering Woodsiders to be more deliberate in developing themselves and the organisation into the future. The commitment to date has seen thousands of employees undertake residential immersions and other learning experiences. Leadership and inclusive leadership capability have been uplifted and the framework has been leveraged as Woodside has successfully transformed operating models and completed a large merger. What was originally a stick drawing drafted on a napkin between two Woodside colleagues is now a conclusive framework known as Navigator. Since its inception in 2021 more than 2600 employees across all five countries have attended a Navigator immersion and more than 2500 Apply Learning experiences have been completed globally. Navigator has expanded its global reach across Woodside’s operations in Australia, China, the USA, Trinidad, and most recently in Dakar, Senegal.
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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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".