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Record W4210564954 · doi:10.1353/nin.2021.0001

Women in Baseball Cards

2021· article· en· W4210564954 on OpenAlexvenueno aff
Tim Wiles

Bibliographic record

VenueNine · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicAmerican Sports and Literature
Canadian institutionsnot available
Fundersnot available
KeywordsLeagueAdvertisingWhite (mutation)Media studiesHistoryArtSociology

Abstract

fetched live from OpenAlex

Women in Baseball Cards Tim Wiles (bio) I can't remember when I began collecting baseball cards with women and girls on them. It certainly grew out of my love for baseball cards in general and my long-time interest in the AAGPBL and women in baseball generally. I remember being interested in the legal fight for girls to integrate Little League in the early '70s, when I was the same age as the plaintiffs, and seeing that reflected in the movies a couple of years later when Tatum O'Neal played the star pitcher in The Bad News Bears. But at some point, I declared women on baseball cards a niche within my card collection. Other niches include cards of players that I've met, cards depicting baseball cards—similar to the stamps on stamps subgenre of stamp collecting—cards with mascots, dogs, or other animals on them, and so on. Professionally speaking, I became a librarian because of my love for baseball. The day after I graduated from college, I saw that week's issue of Sports Illustrated and read with fascination an article about the Baseball Hall of Fame's librarian and their library with three million baseball items. It changed my life. As an Illinois native and Cubs fan, another current in my life had been Harry Caray singing "Take Me Out to the Ball Game" with the White Sox and then with the Cubs. Later, I would be invited to coauthor a book on the song in time for its centennial. So, I was a librarian who collected baseball cards depicting women and who had just written a book about baseball's most famous song. mary marshall In 2009, I got very excited when I learned that eight librarians, six of whom were women, from the public library in Missoula, Montana, had created a set of baseball cards of themselves reading baseball books, a combination of business cards, calling cards, and a fun, quirky public relations tool to get kids interested in the library. I called out to Missoula from Cooperstown and found a librarian to talk to, convincing her to send me a set of these cards for my collection. When it arrived, I was delighted to learn that technical services librarian Mary Marshall had posed holding a copy of my book! It's still the only baseball card depicting a woman on which I make an appearance, though you [End Page 26] would need a magnifying glass to find my name on the book's cover. That's okay, my childhood dream was fulfilled. I had my own baseball card. Kinda. Click for larger view View full resolution Fig. 1. Mary Marshall gabrielle augustine Gabrielle has played baseball for as long as she can remember, usually as the only female member of her team. A right-handed pitcher who occasionally drops down sidearm, she's reached several pinnacles of the game for American women, including playing in Korea for the LG Cup as a member of Baseball For All, and being one of thirty-six women invited to the 2015 United States Women's National Team Trials. Didn't know we had a women's national team? We do, though not because we are such a cool, egalitarian country. Rather, it was forced on us by a world athletic community more interested in gender equity than we are. The team's roots have to do with international competitions demanding that sports allow equal opportunity for women. This card was produced in 2016 as a part of a fundraising set for the Minnesota Girls Baseball Association, the same year that Augustine joined the staff of the National Baseball Hall of Fame in Cooperstown as a curator. Click for larger view View full resolution Fig. 2. Gabrielle Augustine The back of many a baseball [End Page 27] card says that the man on the front enjoys hunting and fishing. Gabrielle's card says that she enjoys knitting. Her favorite artifact at the Hall is the sweater Christy Mathewson wore on the 1913–14 World Tour. "As a knitter," she says, "I love and appreciate the details, such as the finer yarn used to create the...

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)0.002
metaresearch head score (Gemma)0.009
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: Empirical · Consensus signal: none
Teacher disagreement score0.196
Threshold uncertainty score0.656

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0180.004
Scholarly communication0.0170.007
Open science0.0010.011
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.1960.024

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 head0.011
GPT teacher head0.200
Teacher spread0.189 · 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
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
Published2021
Admission routes1
Has abstractyes

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Same venueNineSame topicAmerican Sports and LiteratureFrench-language works237,207