Michael Jordan invests in esports, years after spurning NBA Jam
Bibliographic record
Abstract
A quarter century after being famously excluded from the cult favorite video game NBA Jam, Michael Jordan is investing in esports. NBA legend Michael Jordan is investing in esports, a quarter century after being famously excluded from the 1993 cult favorite video game NBA Jam after he opted out of the players associationâs group license.The former Chicago Bulls star, and current owner of the NBA's Charlotte Hornets, is now an investor with the major esports ownership group aXiomatic.The group's properties include powerhouse franchise Team Liquid.The board at aXiomatic includes Ted Leonsis (lee-ON-sis), who is owner of the NHL's Washington Capitals and the NBA's Washington Wizards.....and Peter Guber (goober), co-owner of the NBA's Golden State Warriors and MLB's Los Angeles Dodgers.NBA legend Michael Jordan is investing in esports. Jordan is now an investor with the major esports ownership group aXiomatic. The board includes Washington Capitals and Washington Wizards owner Ted Leonsis. Peter Guber, co-owner of the Golden State Warriors and Los Angeles Dodgers, is also on the board
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.231 | 0.073 |
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".