Bibliographic record
Abstract
A popular ‘Snickers’ commercial from 1996 shows a hard collision in American football that leaves a player supine on the turf. This is followed by an exchange between the sideline coach and player: The message of the Snickers commercial is that when concussed, a person might believe that they are actually Batman. We are wondering whether Batman himself has ever been concussed and, if so, when asked by Alfred if he knew who he was, might Bruce Wayne answer “an NFL quarterback?”. This paper looks for any evidence of concussion in the big screen representation of Batman that would suggest he might give that type of answer. While historically there have been numerous definitions of ‘concussion’, the most commonly accepted medical diagnostic definition is that derived from the 4th International Conference on Concussion in Sport held in 2012 in Zurich: “Concussion is a brain injury and is defined as a complex pathophysiological process affecting the brain, induced by biomechanical forces”.1 Concussion has been a pernicious, pervasive and under-reported health issue in sport and in public life.2 ,3 Where available, data on the prevalence and incidence of concussion show an overall injury rate of 2.5 per 10 000 athletic exposures4 and statistics from the NCAA show concussion rates increasing by 7% over a 16 year study period.5 Guzkiewicz et al reported a prevalence of ∼5.5% for both Division III and High School Football and that once a player had experienced a concussion, the likelihood of a second concussion that season was tripled.6 Concussion and the long-term risks of …
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.061 | 0.020 |
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".