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Enregistrement W2596842352 · doi:10.1353/nin.2015.0025

Million Dollar Arm by Tom McCarthy

2015· article· en· W2596842352 sur OpenAlexvenueno aff
Ron Briley

Notice bibliographique

RevueNine · 2015
Typearticle
Langueen
DomaineArts and Humanities
ThématiqueAmerican Sports and Literature
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHollywoodLiberian dollarParadeDreamArt historyLegendHistorySuperstarAdvertisingArtPsychologyBusiness

Résumé

récupéré en direct d'OpenAlex

Reviewed by: Million Dollar Arm by Tom McCarthy Ron Briley Tom McCarthy. Million Dollar Arm. Directed by Craig Gillespie. Burbank, ca: Walt Disney Pictures, 2014. 124 min. The 2014 summer-film box office was dominated by evil fairies, mutant super-heroes, and a fire-breathing prehistoric monster. Amid these loud cinematic depictions of violence and fantasy, Walt Disney Pictures released a rather modest baseball film based on a 2007 true story, Million Dollar Arm. The picture, which depicts Hollywood’s nostalgic rendering of the sport as the embodiment of the American dream, opened to mixed reviews and moderate commercial success, earning $10.5 million in its opening weekend up against the blockbuster Godzilla. The reality of the story portrayed in the film, however, raises some serious questions about the American dream that were essentially ignored by the filmmakers. Million Dollar Arm focuses on the career of sports agent J. B. Bernstein (Jon Hamm), who represented such stars as Barry Bonds of the San Francisco Giants and Barry Sanders of the Detroit Lions. In the film, Bernstein’s business is struggling; and he and his partner, Ash Vasudevan (Aasif Mandvi), seize on the idea of finding new baseball talent on the untapped Indian subcontinent. Bernstein assumes that with cricket being so popular in India, there must be bowlers who could throw a baseball. Securing the financial backing of an Asian sports investor named Chang (Tzi Ma), Bernstein develops a contest called Million Dollar Arm in which the winner would receive one hundred thousand dollars, with an opportunity to win a million dollars in the United States and perhaps earn a tryout with a major-league club. Bernstein is assisted in his efforts to find local talent by Amrit Rohan (Pitobash Tripathy), an Indian with a passion for baseball and a desire to coach in the game. Also joining the talent search is aging legendary baseball scout Ray Poitevint (Alan Arkin), who seems to spend most of his time sleeping. The curmudgeonly Poitevint, however, wakes when he hears a fastball pop a catcher’s mitt [End Page 196] at around ninety miles an hour. Poitevint is old-school; and with the exception of a radar gun, he eschews technology and statistical analysis. He knows talent when he sees it and is quite similar to the fictional scout Gus Lobel, portrayed by Clint Eastwood in Trouble with the Curve (2012). After considerable difficulty and some cultural misunderstanding, Bernstein and his team discover two young men with arms that might be compatible with major-league standards. The winner of the contest was the left-handed throwing Rinku Singh (Suraj Sharma), who was one of nine children and the son of an impoverished truck driver. The young man did not play cricket, but Singh was a junior national medalist with the javelin. Finishing second to Singh was the right-handed Dinesh Patel (Madhur Mittal), whose background was similar in that he threw the javelin rather than having been a cricket bowler and in that he came from a poor family in rural India. Bernstein then returns to the United States with his two pitching discoveries and with baseball enthusiast Rohan as an interpreter and chaperone. The agent assigns Singh and Patel to former major-league pitcher and coach Tom House (Bill Paxton), who, with his advanced degree in sports psychology and experience as a coach at the University of Southern California, was expected to teach the young men to play the sport of baseball. Meanwhile, Bernstein, depicted as driving a sports car and enjoying the single lifestyle with numerous attractive women, has his personal life disrupted by the young Indians struggling to deal with a new culture. Unable to leave Singh, Patel, and Rohan in a hotel, where they seem incapable of coping with such modern devices as an elevator, Bernstein is forced to take the Indians into his home, introducing them to such staples of the American diet as pizza. The young men also admire Brenda Fenwick (Lake Bell), a nurse who is renting a guesthouse from Bernstein and eventually becomes a romantic interest for the sports agent. Thus, the Disney film suggests the formation of a family to which Bernstein is initially resistant...

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut 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 enseignants0,617
Score d'incertitude au seuil0,546

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,003
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0040,002
Science ouverte0,0010,002
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,6170,495

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 Opus0,019
Tête enseignante GPT0,211
Écart entre enseignants0,192 · 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 source (Gemma direct ou Codex distillé), 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
Publié2015
Routes d'admission1
Résumé présentoui

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