SPE eMentoring: Expert Career Guidance Across Boundaries and Borders
Bibliographic record
Abstract
SPE 101 If you are, or soon will become, a young member of the oil and gas industry and have questions about which career path to choose, and are looking for guidance in setting and reaching your professional goals, an excellent resource to use is the SPE eMentoring program. For students, mentors can provide academic and career direction. Young professionals (YPs) can receive guidance on how to orient their career progression and diversify their skill sets to speed up their career growth. YPs also have the opportunity to serve as mentors to students. With an online approach, this program helps mentees benefit from the knowledge of experienced professionals from around the world, irrespective of the distances that might separate a mentor and mentee. “Being a mentor in the SPE eMentoring program has been a good and rewarding experience for me,” said Brett Levy, who recently mentored two students, one from Penn State University, and another from Tufts University. “During my mentoring period, I have lived in Denver and Worland, Wyoming, working for Schlumberger, and the program has allowed me to build relationships with my mentees in spite of being thousands of miles away from them. We would communicate regularly via email and LinkedIn to check in on how their classes and projects in school were going, what internships they were interested in, and what career paths they were looking at pursuing after their graduation. I provided as much insight as I possibly could to help them start their careers.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.046 | 0.036 |
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".