Upping the Ante on Technology and Leadership in Challenging Market Conditions
Bibliographic record
Abstract
Pillars of the Industry Part 1: John Wishart, Lloyd’s Register Science and engineering is the lifeblood of my career and a passion since sixth form. It was my teacher who first encouraged me to enroll in engineering, which later led me to qualify with a degree in chemical engineering. My first job was with John Brown E&C in London where I worked on projects in the oil and gas industry. After a few years, my job with BP took me from the engineering contracting sector to a role focused on project and facilities engineering. My next career step was joining the then newly founded Genesis Oil & Gas, moving to Aberdeen and then to Houston, by which time the company was owned by Technip and later I became Technip’s US chief executive officer. While it is true that the mistakes you make along the way help you learn and progress in life, I can say that I have truly gained a lot personally and professionally from the journey I have made and the people I have worked with. When you start out in the world of work, knowing how to take your educational knowledge to the market place and learning how to apply it in different ways in different situations is critical to your success in life. Part 2: Judith Dwarkin, ITG Investment Research As energy in all its forms is essential to both economy and life in general, the challenges associated with finding, harvesting, distributing, and using it efficiently and responsibly are enduring. And whether the current downturn in the global oil market is, or is not, a watershed moment, it is important to remember that as an industry professional you have been presented with a massive summons to action, which also comes with a massive opportunity to shine. I was raised in Fernie, British Columbia, Canada, a small coal mining and lumber town in Western Canada well acquainted with commodity cycles. After completing my undergraduate and master’s degrees, I travelled to Australia on a Commonwealth scholarship to pursue a PhD in natural resource economics. During my academic career, I became interested in public policy and commodity markets and on returning home to Western Canada after graduating, I was fortunate to find a way to pursue both these topics at the Alberta Department of Energy. At the time, the provinces and Canada’s federal government were in the process of dismantling a plethora of energy policies and regulations that imposed various types of controls on domestic oil and gas upstream development and markets, as well as internal and external trade. This period of deregulation was a dynamic and challenging time to work on energy policy and, as it turned out, a great entry point for my career in the industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".