Bibliographic record
Abstract
The Way Ahead Interview - A conversation with energy investment banker Matthew R. Simmons. What was your first job, and what were your impressions of the oil and gas industry when you started working? I accidentally stumbled upon my first job. When I was in my second year at Harvard Business School, I reluctantly signed up for a course called Manufacturing Policy. I say reluctantly because of all of the courses I took in my first year, Manufacturing Operations was singularly the most boring. But during a conversation with a finance professor, he recommended this as the best finance course of the second year. A few days later, the school paper listed all the secondyear courses, and Manufacturing Policy was rated as the course with the hardest workload in all second-year courses, so it was with great reluctance that I signed up. Upon completion of the course, before our grades were issued, I was asked to meet with the professor—I was positive I had flunked the course. I was astonished when the professor asked if I would stay on and work at the school as a research associate to rework the case studies in this particular course. This detour is what stopped me from returning to Utah and becoming a commercial banker, which had been my chosen career path when I enrolled. As it turned out, several of the case studies I worked on during this two-year program were on oil companies. The first was on Phillips Petroleum betting its future on building the world's largest ethylene plant. I then did a case on a new refinery being built in Come-by-Chance, Newfoundland, and another on a merger between three very tiny exploration, production, and refinery companies based in Wyoming called Tesero Petroleum. Frankly, none of this work heavily influenced me in getting interested in oil and gas. I was actually nearing the end of my second year as a case writer when I was on my way to Los Angeles to write a case on American Cement and I stopped in Palm Springs for the weekend. My father, a commercial banker in Utah, was in Palm Springs attending a mergers and acquisitions seminar. When I arrived to spend the weekend with my parents, Dad told me about a really interesting young guy in our class who was apparently a deep-sea diver. When I heard "deepsea diver," I assumed he was probably a treasure diver and I was really keen to meet him. So, the next day during the seminar coffee break, I introduced myself to Laddie Handleman. Unknown to me at the time, this chance introduction became my introduction to the oilfield service industry. It turns out that Laddie had founded a company called Californian Divers ("Cal Dive"). The company had grown so fast that they were running out of money and were talking about being acquired by Santa Fe. I asked him why he was considering selling and if he had instead thought about raising some venture capital. I told him that I could probably find a few investors who could put enough capital into his company to give it 2 or 3 more years of growth, and then he could sell the business.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.043 | 0.013 |
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".