The top ten similarities between playing hockey and building a better internet
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Time tends to pass more quickly than we would like. Sometimes it is helpful to reflect on what you have accomplished, and to derive what you have learned from the experiences. These "lessons learned" may then be leveraged by yourself or others in the future. Occasionally, an external event will motivate this self reflection. For me, it was the 50th anniversary reunion of the St. Walburg Eagles, held in July 2011. The Eagles are a full-contact (ice) hockey team I played with between 1988 and 1996 (the Eagles ceased operations twice during this period, which limited me to four seasons playing with them), while attending university. What would I tell my friends and former teammates that I had been doing for the past 15 years? After some thought, I realized that my time as an Eagle had prepared me for a research career, in ways I would never have imagined. This article (an extended version with color photos is available in [1]) shares some of these similarities, to motivate others to reflect on their own careers and achievements, and perhaps make proactive changes as a result.
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.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it