President’s Column: How Members Can Help Improve the Industry’s Public Image
Bibliographic record
Abstract
Editor’s Note: This is a summary of the April episode of the President’s podcast. We encourage you to listen to the episode to hear the full conversation. In this podcast episode, I am joined by Paige McCown, senior manager of communication and energy education, to discuss the importance of how SPE engages with external stakeholders to help improve the industry’s public image, particularly through programs like energy4me. I start the episode by emphasizing how the industry’s public image is a critical concern for our future, and it’s a common question among members about what SPE can do to help. Although environmental scrutiny of the industry has always been present, it has become more pronounced with the increased focus on climate change and emissions. The energy industry has made many positive impacts across the world, from providing energy for almost everything we do to producing everyday products and conveniences. I also believe that the industry is best positioned to solve today’s energy challenges. However, two main problems related to our public image hinder our ability to meet these challenges: our ability to attract the best talent, and our ability to attract investments in technology and innovation. The discussion turns to ways to change public perception. I review some of the topics discussed at the Presidents Panel that I participated in at IPTC 2024. These include how engaging students at a young age and making them aware of the benefits of the industry helps change the narrative at an early age. Also, it is important to engage teachers, guidance counselors, and communities in promoting our positive aspects. The conversation moves to how SPE is equipping its members to engage in these efforts. I highlight SPE’s energy4me program which provides an exploration and production curriculum and hands-on activities to teach the science behind the industry. To accompany the program, SPE worked with DK Publishing to publish the Oil and Natural Gas book, available in nine languages, which helps convey the importance of the industry and provides talking points for members to use in their communities. SPE deploys the program through workshops at key events, virtual workshops, and the Energy4me Ambassador program. Sections and chapters across the globe are active in this program, so in 2022 SPE created the Corporate Ambassador program. Companies like ExxonMobil, EOG Resources, and Aramco Americas participated in the program. The program can help some companies meet their corporate responsibility and community outreach goals. I also spend time discussing how local sections and chapters are using the program in their communities. We highlight various initiatives including STEM education programs, workshops for teachers and guidance counselors, and outreach events like Family Science Day. The Calgary section YPs organize an annual Family Science Day, aimed at educating students about oil and gas and putting on fun experiments.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".