Guest Editorial: We Need To Celebrate Our Successes if We Want the Next Generation To Be Part of Our Future
Bibliographic record
Abstract
Guest editorial The prolonged downturn in commodity prices that started in the summer of 2014 has seen more than 350,000 people globally lose their jobs in the oil and gas industry to date. However, as BP’s mantra “lower for longer” evolves into Shell’s phrase “lower forever” and we adapt to the reality of working with an oil price around $50/bbl, signs of recovery are beginning to occur. The US shale sector, with its relatively low operational expenditure costs and rapid rig up time, has led that revival, and it is anticipated that deepwater projects, which have also experienced cost reductions over the past 3 years, will follow in time. However, in line with every other time this cyclical sector has come out of a downturn, the anticipated increase in activity is matched by concerns over how and where the industry will find the recruits required to fuel a resurgence, either through attracting former employees back or encouraging new, young people to join. Attempts to attract new employees can be hampered by negative perceptions of oil and gas in some parts of the world. This challenge was reinforced recently by a poll from Ernst & Young (EY) among 1,200 consumers and 100 industry executives in the US. Held in the first quarter of this year, the US Oil and Gas Perception poll found that only 26% of Generation Z (aged 16–19) and 45% of millennials (aged 20–35) without a set career path found jobs in the industry appealing. A surprising 39% of teenagers actually ranked it as “very unappealing.” Two out of three believed the industry causes problems rather than solves them and just 36% thought it had a positive effect on local and global communities. Furthermore, the majority from the younger generation perceived oil and gas jobs to be blue collar, dangerous, and physically demanding, and just 39% thought it provided long-term financial stability. Such assumptions do not extend to every corner of the world (there is generally considerable interest and enthusiasm in Africa and Asia, for example), but one could argue that a perception challenge toward the benefits of working in oil and gas does exist within societies in Western Europe and North America. As an industry, our efforts to promote the positive and exciting aspects of our job have often fallen short of convincing the wider public of the crucial role oil and gas plays in sustaining the global economy and modern life. As a result, compared with other industries, we do have to work harder to recruit for some roles. Maybe we take for granted the technical achievements that many companies make to overcome ever-more-complex challenges and that often astound the outside world when it hears of them?
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".