Bibliographic record
Abstract
President's column SPE can help small companies navigate financial understanding Half of my career has been in the producer side of the business and the other half with a service company. The service experience has been with large organizations. It’s rather a nice place to be. My producer experience has been primarily with tiny, small, and intermediate companies. The experience quite literally has highs and lows. Imagine a cartoon character jumping off a cliff—at first, you feel like a bird but as the ground approaches you are terrified and shortly thereafter you are quite compact, so to speak. I have had moments of pure euphoria as a small producer (usually the 15 minutes after a capital raise or a sale event), but more often it is panic, terror, and a lot of out-of-control times when you wished you had a finance degree or two. It may not seem intuitive, but engineers need to understand financial matters, and that’s where SPE comes in, with connection opportunities to places other than your technical discipline. There are a lot of us technical types trapped or living in the small cap producer world or technical innovator space. We don’t get much support at technical conferences. We don’t get much time to do technical things. Little producers are very often forgotten. Small technology providers rarely get love, and often they have little time allotted to pitch their idea. I’m going to pay some attention to that group in this column. I’m going to promise some good things in the future. Early in my career—when the local price of oil was less than $10/bbl and the future was extremely bleak—I was fortunate enough to encounter people who held very lofty positions and took the time to care about this lonely new grad. In the early 1980s, Gerry Maier O.C. (the Order of Canada) was president and CEO of TransCanada Pipelines, a large public company located in an office towering above Toronto’s financial district. I was a struggling new grad working on the periphery of the industry, but Maier took me under his wing. He gave me all the attention I needed to discuss establishing a petroleum engineering chair at the University of Alberta, our alma mater. Even after all these years, Maier has not forgotten that meeting. He has met with other CEOs, Canadian prime ministers, and even royalty, yet Maier remembers that meeting with me. He has no idea how it kept me going at one of the darkest moments of my career. He remembered the little guy. To this day, I try to pay back that generosity by modeling that behavior. For example, I hope that Tarun Kumar from UPES in Dehradun, India, is encouraged for life because I took the time to encourage him, after disagreeing strongly with a technical point he made in a student paper competition. Maybe someday he will be involved in a small cap Indian producer listed on the Mumbai exchange.
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 | 0.028 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.021 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.034 | 0.006 |
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".