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Record W175519946

Triple Play: Personal Reviews, Op-Ed Pieces, and Polemics from outside the Purview of the Umpires

2006· article· en· W175519946 on OpenAlexvenueno aff
Darryl Brock, Bob Boynton, Karl Lindholm

Bibliographic record

VenueNine · 2006
Typearticle
Languageen
FieldArts and Humanities
TopicAmerican Sports and Literature
Canadian institutionsnot available
Fundersnot available
KeywordsLeagueFantasySpellThe ImaginaryAction (physics)nobodySimple (philosophy)Visual artsPsychologyAestheticsAdvertisingMedia studiesArtComputer scienceSociologyPsychoanalysisLiteratureEpistemologyComputer securityPhilosophyBusiness
DOInot available

Abstract

fetched live from OpenAlex

Leagues of Their Own Why would anybody spend long hours hunched over a flat surface, tracking the progress of imaginary baseball games? No simple answer may suffice, but since practitioners by the tens of thousands--some of them famous--have engaged in this unlikely pastime, it probably can't be dismissed as sheer insanity. Long before today's fantasy leagues--which entail gambling far more than fantasy--kids of all ages fell under the spell of commercially manufactured tabletop baseball games. These were combinations of boards, dice, cards, and spinners used to simulate contests among actual Major League players. Equally important, the simulations allowed mixing and matching from diverse eras: 1927 Yanks vs. the Big Red Machine? Koufax vs. Cobb? Mathewson vs. Pujols? No problem. Create customized leagues? A cinch. To onlookers, the action might appear to be on the tabletop, but in fact, it unfolded vividly in game players' imaginations. The first of these games appeared in the early 1940s as Ethan Allen's (later Cadaco) All-Star Baseball, a simple affair with player disks that fit over a spinner. The disks were calibrated according to the player's lifetime stats. Where the arrow of the spinner came to rest indicated the play result. Naturally the space for a home run on Babe Ruth's disk was wider than on any other. A decade later brought Strat-O-Matic and its main competitor, the American Professional Baseball Association (APBA). These relatively sophisticated simulations used dice and player cards and charts to replicate not only the batting prowess of big leaguers but also the pitching and fielding tendencies. Game players handled all managerial duties, such as selecting lineups, dictating offensive and defensive tactics, and keeping statistics afterward if they wished. Those tabulations, computerized now, were long, hands-on labors of love. Over time these games took on enormous cult popularity. Leagues formed across the country. Each fall, champion APBA teams and their managers met in a World Series tournament; each February, APBA fanatics braved snowstorms to line up outside the company's factory in Lancaster, Pennsylvania, to receive the latest cards. Writer-editor Daniel Okrent (who as an adult would invent Rotisserie League Baseball) recalls the wait for a new season's Strat-O-Matic cards as agonizing. To the uninitiated, he concedes, the codified cards might have looked like hieroglyphics; but to the young Okrent, they were as thrilling as an epic poem. From my first roll of the dice, I was hooked, says Jon Miller, San Francisco Giants and ESPN broadcaster, in Confessions of a Baseball Purist. After spending long months replaying the entire 1966 National League schedule with his Strat-O-Matic game, Miller proceeded to make up his own league with franchises in Hudson Bay, Rome, London, and even Machu Picchu. By then, presaging his future profession, he'd taken to announcing games--even imitating background sounds: PA system announcers, vendors' cries, infield chatter, and rousing stadium cheers. Miller's father, passing by his closed bedroom door, feared the boy was having a heart attack. Partly because his mother grew tired of stepping over him on the floor, Joe Torre, former All-Star catcher and current Yankees manager, played APBA in the basement of his boyhood pal, Johnny Parascandola. The pair established a league and kept stats. We spent hours upon hours down there, including a good chunk of our winters, Torre says in his memoir, Chasing the Dream. Even then I enjoyed the decision-making involved in managing. Now, many years since his playing days, Torre still sometimes receives his own APBA card with an autograph request. Whenever I see one, he says, it's like opening an old photo album. Torre stayed cool in tight APBA games--but not his buddy Johnny, who might upend the table if he lost. Once Johnny stuck a pitcher's card under a running faucet, yelling I'm sending you to the showers! …

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0020.003
Scholarly communication0.0070.006
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0500.037

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.

Opus teacher head0.014
GPT teacher head0.206
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations1
Published2006
Admission routes1
Has abstractyes

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