Is it Luck or Hard Work that Merits Success? What if it’s a Little bit of Both?
Bibliographic record
Abstract
The cut-off registration date for the youth Canadian Hockey League is January 1. Due to this being the program’s regulations, most of the kids signing up and making the team are born in January, February, and March. Their birthdays being earlier in the year provides them with more experience and more time to perfect their hockey skills than the other boys who sign up for the league. Imagine that the month you are born puts you ahead of hundreds of other boys fighting to achieve the same goal as you. Because you were born earlier in the year, you have extra months to develop your hockey skills, which inevitably puts you ahead. Having an advantageous birthday may seem like pure luck, but it’s not only a birthday that makes a good hockey player; players must put in a deserving amount of effort to make the team. Then there’s Bill Joy and Bill Gates: two men born in an extraordinary time when they received the opportunity to be a part of great advancements in technology. Maybe there was some element of luck in being born at the right time, but what about the thousands of hours of effort they put into their careers? Did luck or work put them at the top of their fields?
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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".