SPE Strong: Strengthening Our Core: The Influential Yet Delicate Balance of Mentoring
Bibliographic record
Abstract
President's column “The delicate balance of mentoring someone is not creating them in your own image, but giving them the opportunity to create themselves.”—Steven Spielberg One of my focus areas as SPE President is strengthening the feedstock of incoming talent into our industry. In my January column, I talked about industrywide initiatives under way to attract and retain this talent. This month, I want to engage with our members on the importance of effectively mentoring these bright, young minds. While I reflected upon setting my goals for the new year, I began to think about all of the important people in my career who not only helped me set high goals, but also provided support and guidance in achieving those goals. Some people were assigned to be my mentor, while others did it informally by providing advice during opportune moments. Truthfully, it does not matter how they became my mentor. The key point is that they were. Coaching a young professional in a technical position is critical to their success, but developing a person to have the interpersonal skills to navigate this industry is equally important. For 2020, my goals include being both a mentor and mentee. While I believe I have much to contribute to our younger members, I also have much to learn. I want to encourage more of our members to set goals around mentoring and want to make this the month where we take time to recognize and thank those who have had a positive impact on us as our mentors. Thank you, Don Patterson (retired Chevron), for mentoring me through my years of supervising workover rigs in northern Alberta. You were tasked with mentoring all the young engineers who were supervising rigs in northern Alberta, and phoning in our morning reports to you was often the most anxious part of our day. After looking back, your critiques were the necessary constructive feedback we needed. Fred Brownlee (retired Chevron) and John Patterson (retired ConocoPhillips) were two of my most influential mentors, both in helping me grow my technical skills and for connecting me with industry peers. Mike Mooney (retired ConocoPhillips) taught me how to be a great manager, how to conduct employee performance assessments properly, and how to set my focus on value-adding goals. Karen Draper (retired S&N Pump) and Carol Magney Grande (Magney Grande) provided mentorship on how to succeed in this industry as a working mother, and also gave me my first opportunity to serve on an SPE committee. Finally, I must thank Dr. Jeff Spath (SPE 2014 President). He became my mentor when I first served on the SPEI Board as a Technical Director and has continued to provide critical guidance and constructive feedback since. Without his help, I would not be writing this column as your 2020 SPE President.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".