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Record W2162679077 · doi:10.1353/nin.2007.0024

Attitudes of Citizens Fifty-Five Years and Older with Regard to Major League Baseball

2007· article· en· W2162679077 on OpenAlexvenueno aff
George W Schubert

Bibliographic record

VenueNine · 2007
Typearticle
Languageen
FieldArts and Humanities
TopicAmerican Sports and Literature
Canadian institutionsnot available
Fundersnot available
KeywordsLeagueRespondentDemographyPsychologyMedicineGerontologyPolitical scienceSociologyLaw

Abstract

fetched live from OpenAlex

Attitudes of Citizens Fifty-Five Years and Older with Regard to Major League Baseball George Schubert (bio) Three thousand questionnaires and response forms were sent out in 2003 to senior citizens, and an additional three thousand questionnaires were mailed to seniors in 2004. Senior citizen addresses were randomly selected from lists provided by senior organizations in cities with Major League baseball clubs. Of the 3,000 questionnaires mailed in 2003, 2,126 response forms were completed and returned, and 798 forms were not returned. In addition, 76 response forms indicated that the respondent had no interest in baseball and, therefore, were not included in the tabulation. Of the 3,000 respondents in 2004, 876 forms were not returned and 84 indicated "no interest in baseball" and were not tabulated. Approximately two-thirds of the respondents were male. The following subjects were addressed. 1. Eighty-five percent of the males and seventy-four percent of the females responded that their attitude toward MLB was negatively affected by the use of prohibited substances by MLB players. 2. Twenty percent of the males and thirty percent of the females indicated that convicted felons playing in MLB negatively affected them. 3. Forty-one percent of the females, in contrast to fifty-nine percent of the males, reported the Baseball Players Union had a negative influence on their opinion. 4. The majority of respondents did not perceive MLB players as heroes or good role models. The respondents were asked to cite players who they thought were good role models. The four American League players most often listed as a good role models/heroes were Derek Jeter, Ichiro Suzuki (804), Curt Shilling (711), and Darin Erstad (627). In the National League, [End Page 166] Sammy Sosa was cited the most often (847), followed by Chipper Jones (745), Todd Helton (655), and Tom Glavine (540). 5. An open-ended question suggesting changes that could be made to increase interest in MLB proffered the following suggestions: standardize the playing field, create team parity, have umpires call the game "according to the book," and eliminate fighting. 6. Respondents provided the following suggestions to increase attendance at MLB games: reduce ticket and parking costs, reduce the distance from the parking lot to the stadium, reduce the cost of beer. 7. The respondents of both genders were more interested in MLB ranking ahead of the NFL, the NBA, and the NHL 8. The respondents were asked to pick the four most exciting plays in baseball. 9. When asked to pick the four most exciting plays in baseball, the respondents chose the following: the triple play (3,106), the unassisted triple play (2,841), stealing home (2,112), home runs (1,684), hit and run (1,656), and the double play (427). . Forty-one percent of the males and forty-three percent of the females believed the game was enhanced by the increased presence of international players. . Seniors cited several reasons for attending, including more spare time and a longtime interest in MLB. . Only twenty percent of the males and twenty-two percent of the females thought player free agency was good for the game. . Nearly two-thirds of the respondents held an overall negative view of what they felt was an aloof, nonchalant, and apathetic attitude of the players. George Schubert George W. Schubert is Professor and Dean Emeritus at the University of North Dakota. He has authored books, book chapters, and over seventy refereed published papers. He retired from the University of North Dakota in 1999 but continues to research, write, and publish. Copyright © 2007 the University of Nebraska Press

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.000

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 head0.008
GPT teacher head0.218
Teacher spread0.210 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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

Citations0
Published2007
Admission routes1
Has abstractyes

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