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Enregistrement W4294315057 · doi:10.1353/nin.2022.0028

The Baseball 100 by Joe Posnanski

2022· article· en· W4294315057 sur OpenAlexvenueno aff
Willie Steele

Notice bibliographique

RevueNine · 2022
Typearticle
Langueen
DomaineArts and Humanities
ThématiqueAmerican Sports and Literature
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLeagueStadiumArt historyArtFootballGeorge (robot)HistoryMedia studiesSociologyLawPolitical science

Résumé

récupéré en direct d'OpenAlex

Reviewed by: The Baseball 100 by Joe Posnanski Willie Steele Joe Posnanski. The Baseball 100. New York: Avid Reader Press, 2021. 869 pp. Cloth, $40.00. If there is anything baseball fans love more than the sound of a wood bat hitting a ball or the smell of hot dogs at the ballpark, it’s arguing. Fans will argue over whether a pitch was a ball or a strike. They’ll argue over which stadium is the best in the majors. They’ve been known to argue about which minor league team has the greatest mascot. And they have even argued over which suffered the most before finally winning a recent World Series, the Cubs or the Red Sox. In the introduction to The Baseball 100, commentator George F. Will begins by saying, “Baseball fans are an argumentative tribe” (1). And somewhere, probably in Cleveland, I can hear a fan respond, “No we aren’t!” If you are a reader who is looking for Joe Posnanski’s latest book, The Baseball 100, to solve any arguments, don’t get your hopes up. If you are looking for [End Page 142] a book to start a lot of arguments, more often than not between the reader and the author, you won’t be disappointed. And if you are looking for a book that reminds you why baseball has the greatest collection of characters in sports history, you’ve come to the right place. The book is, as its title indicates, Posnanski’s list of baseball’s top one hundred players. The chapters were originally written as individual articles over a hundred-day stretch when they were published on the Athletic website. Rather than simply relying on advanced statistics, and there are certainly plenty of those to go around, Posnanski incorporates the roles players played in the game’s rich (and often complicated) history, interesting sidebars about their lives, and elements of their lives and abilities that made them stand out from the thousands of players who did not make the list. In his introduction to the collection, the author recounts a story of when former Major League Baseball (MLB) commissioner Bud Selig called him to dispute the rankings that had appeared on the website. Rather than defending his choices, Posnanski readily admits to the reader, “You will have bones to pick, too” (6). He knows you’ll disagree with the list, and he’s okay with it. Unlike nearly every other book, The Baseball 100 is missing a table of contents, making it impossible for readers to jump ahead to the players they are most interested in or to see what players Posnanski has ranked and in what order. But instead of being put off by this tool, this reader enjoyed not knowing who was coming next in the book until he had finished the current chapter. (I will admit, however, that I skipped to the end to see who he’d ranked number one.) Perhaps the most interesting aspect of this book is the variety of players Posnanski chose for his list. The first chapter, or the hundredth best player, is (spoiler alert!) Ichiro Suzuki. And the last chapter, or the number one player to have ever played baseball, is (another alert!) Willie Mays. In between, there are players from MLB and players from the Negro Leagues, players from the Deadball Era and the modern era, players who are identifiable by a single name (“Babe”), and others whom you have most likely never heard of. Posnanski’s list is as much a tutorial of baseball’s history as it is a reminder of what makes its most iconic players so memorable. Throughout the book Posnanski gives readers glimpses into those players with whom he became acquainted in his career as a sportswriter, explains the memorable moments associated with many of the players, and debunks some long-standing myths associated with others in the game. The result is a compilation of players that is part biography, part statistical analysis, part mythology, and all baseball. Readers will find themselves appreciating the complexity in each chapter while arguing “Yes, but what about . . .” multiple times. [End Page 143] Some of the lengthier chapters are...

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,222
Score d'incertitude au seuil0,779

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,2220,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,007
Tête enseignante GPT0,186
Écart entre enseignants0,179 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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