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Record W3129556864 · doi:10.1353/nin.2019.0013

Reflections on a Flawed Baseball Idol

2019· article· en· W3129556864 on OpenAlexvenueno aff
Ken Reed

Bibliographic record

VenueNine · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicAmerican Sports and Literature
Canadian institutionsnot available
Fundersnot available
KeywordsLeagueArt historyCharacter (mathematics)White (mutation)ArtPerformance artMedia studiesVisual artsHistorySociology

Abstract

fetched live from OpenAlex

Reflections on a Flawed Baseball Idol Ken Reed (bio) As a child, I picked my sports idols based on how far and how dramatically they hit home runs (Reggie Jackson), how cool-sounding their names were (Roman Gabriel, Bob McAdoo), how sweet their uniforms looked (Roger Staubach), and how many championships they’d won (Bill Russell). Today (as a maturing adult, or aging geezer, take your choice), I see the shallowness in that. Today, it’s their character—on and off the field—that is my top criteria in picking the athletes/coaches I admire. People like John Wooden, Jackie Robinson, Cal Ripken, Dean Smith, Harmon Killebrew, Billie Jean King, Byron Nelson, Tony Dungy, David Robinson, Steve Nash, Julie Foudy, Grant Hill, Annika Sorenstam, Steve Kerr, and Brad Stevens. But when you’re a kid, there often are more important things to you than character. As a Denver native, I grew up without Major League Baseball (MLB). My first favorite team was the Minnesota Twins because my cousins lived in Duluth and when we’d visit, my uncle would take us to a Twins game. But everything changed for me on July 13, 1971, while watching the MLB All-Star Game on television with my dad. Going into the game, I had become intrigued by the Oakland A’s flashy new kelly green and Ft. Knox gold uniforms and matching wedding gown white shoes. (Yes, that’s how the A’s idiosyncratic owner, Charles O. Finley, described his team’s colors). The A’s colorful uniforms were a stark contrast to the old gray and white uniforms of the day and an appealing symbol of rebellion for someone a few months shy of his thirteenth birthday. In the bottom of the third, Reggie Jackson came up. He was quickly down two strikes but then crushed a pitch from the Pittsburgh Pirates’ Dock Ellis off a light transformer on top of the Tiger Stadium roof. It was an awe-inspiring shot that most people projected at around 550 feet. A Wayne St. University researcher went one step further, pegging the blast at 650 feet. I was hooked. The A’s were my team and Reggie was my guy. I had the joy of watching the A’s go on to win five division championships and three World Series titles in the ’70s. [End Page 53] Recently, I sat down in my study to write a column about the death of sports-manship (okay, it isn’t dead but it’s certainly on life support). I hit a lull in my writing and started glancing around the room at my bookcases. At the top of one shelf sat “Big Blue.” Big Blue is my trusty ol‘ Baseball Encyclopedia, a massive book with a beat-up blue hardcover. Before I knew it, I had pulled Big Blue down and was flipping through its pages. I landed first on Ernie Banks’s page and felt a tinge of sadness due to the fact Banks never got a shot at the World Series. I continued moving through Big Blue, stopping occasionally to look at the stats of both former stars and obscure players whose names I had forgotten. Ultimately, I landed on Joe Rudi’s page. I’ve always thought Rudi was the unsung hero of that ’70s A’s dynasty. He was a consistent player, offensively and defensively, and always seemed to come up with a big play in tight games. Perusing the Baseball Encyclopedia is a regular ritual for me, especially in the spring, when my childhood passion for the game always comes back. There’s something about baseball that affects me like no other sport. No matter how mad I get at the Lords of Baseball, despite the greed of agents, players, and owners, despite .250-hitting multi-millionaire infielders with bad attitudes, every spring the baseball bug bites me again. I think Jim Bouton nailed it when he said, “You spend a good piece of your life gripping a baseball and in the end it turns out that it was the other way around all the time.”1 But I digress. On this particular excursion through Big Blue, I ended up...

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)0.007
metaresearch head score (Gemma)0.016
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: Other · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0180.008
Scholarly communication0.0110.006
Open science0.0040.011
Research integrity0.0130.034
Insufficient payload (model declined to judge)0.0260.010

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 head0.026
GPT teacher head0.270
Teacher spread0.245 · 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
GenreOther

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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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