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Record W4255136729 · doi:10.1353/nin.2020.0022

The Baseball Bat: From Trees to the Major Leagues, 19th Century to Today by Stephen M. Bratkovich

2020· article· en· W4255136729 on OpenAlexvenueno aff
Jennifer J. Asenas, Kevin A. Johnson

Bibliographic record

VenueNine · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicAmerican Sports and Literature
Canadian institutionsnot available
Fundersnot available
KeywordsLeaguePopularityForesterArt historyHistoryArtGeographyLawPolitical science

Abstract

fetched live from OpenAlex

Reviewed by: The Baseball Bat: From Trees to the Major Leagues, 19th Century to Today by Stephen M. Bratkovich Jennifer J. Asenas and Kevin A. Johnson Stephen M. Bratkovich. The Baseball Bat: From Trees to the Major Leagues, 19th Century to Today. Jefferson, NC: McFarland, 2020. 198 pp. Paperback, $29.95. In The Baseball Bat, trained forester and wood enthusiast Stephen Bratkovich conducts extensive research into bat manufacturing. He visits several manufacturers who "opened their doors and welcomed 'the rookie' with open arms. Private tours, photographs, answers to endless questions," and provided review and feedback to the book chapter drafts (v). The book is primarily "focused on wooden baseball bats and the trees that produce them" (3). [End Page 215] Chapter one traces the wood bat to historical games that involved striking objects with sticks. Although thought to exist as far back as 2400 BC, pinpointing the exact origin of the game we call "baseball" today is complicated. As Bratkovich notes, "bat and ball games played by youngsters have likely been around as long as we've had youngsters. Whether these games were played with a simple stick, small limb, or merely a hand as the 'bat' is open to argument" (6). This chapter delves into the twists and turns of the historical narratives that have evolved concerning the popularity of wood bats in ball and stick games. Chapter two traces the evolution of bat design and material preferences. Bratkovich identifies one consistent truth: "Though baseball and its people have changed, the use of wood to make bats has not. In fact, at the highest level of baseball the object used to strike the ball is fashioned from a tree" (14). Players have used wood bats made from several different kinds of trees including sycamore, cherry, spruce, chestnut, poplar, basswood, willow, ash, maple, pine, and hickory. Chapter three examines the history of companies that used these types of wood to make bats. The chapter discusses economic forces in the bat industry through an investigation of companies like Hillerich & Bradsby (known famously for the Louisville Slugger), Spalding, Wright & Ditson, and A. J. Reach Company. Chapter four traces the evolution of bat makers after World War II from the larger companies to the boutique. The chapter examines the impact of the aluminum bat on the market of wood bats. For example, with the rise of aluminum bat companies like Worth and Easton, "Hillerich & Bradsby's wooden bat sales dropped almost overnight from seven million to one million per year" (51). The market forced Louisville Slugger into the metal bat market because the MLB market that required wooden bats was not enough to sustain the company. In chapter five, Bratkovich makes a turn to study player preferences and the wood itself. He explaines, "It's important to remember that wood comes from trees … Wood evolved as a functional tissue of plants and not to satisfy MLB or the players who swing the objects known as bats" (58). Regardless of the type of wood, the perfect bat is hard to define. Bratkovich draws on former MLB player Scott Podsednik's statement in the New York Times: "'You can't describe it—it's a feel. When you pick it up and take a couple of swings with [End Page 216] it, you just know.' When this occurs with players, as Podsednik described, all information and scientific data about wood properties and features are out the door" (71). Whatever bat the player chooses, they will always be prone to breakage. Chapter six examines much of the scientific data and processes that are in place to protect players from broken bats. In explaining that the goal is to reduce bat breakage, he notes, "Wooden bats will always break due to poor wood quality, prolonged bat use, incorrect contact with the ball, pitch speed, and other reasons too numerous to name" (84). Chapters seven and eight discuss forests and the threat of pests to the trees in those forests. Tailoring the analysis of forests to each wood type, Bratkovich provides many details that play into the construction of a high-quality wood bat. For example, "The best white ash baseball-bat trees grow on the north...

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 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: Other · Consensus signal: Other
Teacher disagreement score0.317
Threshold uncertainty score0.967

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)0.0330.000

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 head0.010
GPT teacher head0.192
Teacher spread0.183 · 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
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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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