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Record W1490254684

UNION WOMEN: Forging Feminism in the United Steelworkers of America

2006· article· en· W1490254684 on OpenAlexvenueaboutno aff
Patricia Baker

Bibliographic record

VenueResources for feminist research · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicLabor Movements and Unions
Canadian institutionsnot available
Fundersnot available
KeywordsFeminismGender studiesSociologyWorld War IIWhite (mutation)Feminist movementLawPolitical science
DOInot available

Abstract

fetched live from OpenAlex

UNION WOMEN: Forging Feminism the United Steelworkers of America Mary Margaret Fonow Minneapolis: University of Minnesota Press, 2003; 251 pp. Union Women: Forging Feminism the United Steelworkers of America tells the story of the rise of union feminism the United Steelworkers of America (USWA). Mary Margaret Fonow's telling of this story is important for several reasons. The USWA is a large international union, with a membership across both the United States and Canada. Because the union's membership has traditionally been blue-collar, male workers, an understanding of the development and growth of union feminism the USWA must explore the history both of the appearance of non-traditional occupations the steel industry, and of women's mobilization and collective identity formation as both unionists and feminists. In her book, Fonow charts how these two histories have unfolded and intertwined, drawing on both feminist and social movement theory to understand when, where, how and why working the steel industry were able to come together effectively to act their own interests. Fonow traces the often uneven and difficult struggle of to gain access to jobs once held almost exclusively by men the steel industry. In the years before World War II, few jobs were open to the industry. In World War II, white and of colour were hired the steel industry larger numbers, generally less-skilled jobs that were often refashioned to make them more 'suitable' for - with the important exception of black women (p.44), who like black men were only able to obtain more dangerous and less desirable jobs. Thus race, ethnicity and gender were significant organizing and stereotyping categories among and between and men the steel industry. During the war joined the USWA in record (p.44). Following the war, most Steelworkers were let go or assigned to gender-segregated departments. Women continued to join and become involved unions, but largely femaledominated unions retail, communications, food service and clerical occupations, where were finding jobs. Working-class union feminism flourished the 1950s ... [but] [t]he feminist anchors this period were the female-dominated unions and not steel. (p.41) Fonow effectively describes and assesses the legal and political context that led to the next significant period of women's employment the steel industry, the 1970s, At this time the United States, civil rights law and a political discourse of equal opportunity enabled the introduction of a court-ordered, affirmative-action consent decree, with timetables and goals for hiring nine major American steel companies. This consent decree allowed large numbers of to enter the steel industry, although affirmative action initiatives like the decree were opposed by some white male workers. In Canada, relied upon human rights legislation to gain access to jobs steel companies. Fonow explores the different ways which these processes - differently established and received the two countries - provided with access to non-traditional, better-paying jobs the steel industry. Moreover, she illustrates the impact of these forms of legislation and the social movements actively engaged creating the legislation on male-dominated unions such as the USWA, that were influenced to change their own discriminatory practices. One of the strengths of Fonow's book is that she tells the stories of many who worked the steel industry throughout the years; for example, she describes and assesses the experiences of the first hired by Wheeling-Pittsburgh Steel under the 1974 consent decree. These women, and many others like them, struggled to take their place within an industry and a union dominated by men, though often they did not see themselves or their efforts as feminist. …

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)0.005
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.055
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0250.024
Scholarly communication0.0100.007
Open science0.0010.007
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0070.001

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 head0.049
GPT teacher head0.382
Teacher spread0.333 · 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 designQualitative
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

Citations4
Published2006
Admission routes2
Has abstractyes

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