Bankers and bomb makers: gender ideology and women's paid work in banking and munitions during the first world war in Canada
Bibliographic record
Abstract
During the First World War, some Canadian women found themselves in new and unfamiliar environments, doing jobs apparently unavailable to them before the war. Many of those women were successful in the new opportunities available to them. The focus of this study is twofold. First, it examines the scope and the nature of women's work in two industries, banking and munitions, during the war. This is an important step because we still know very little about women's experience of the war. Understanding how many women worked and in what capacity is essential to understanding the nuances of women's wartime experience. Women who worked in banking and munitions were not a homogeneous group. The composition of the wartime workforce is also analysed. The war's impact on wage rates for women is also examined. Second, the study focuses on the nature of the impact of wartime participation on gender ideology. In particular, the study seeks to determine if gender ideology was affected by women's expanded opportunities in masculine occupations during the war. Often, the historiography regarding women and war is characterised by a binary discourse that seeks to determine whether on not wars liberate women. Rather than engage in that debate, this study attempts to avoid it as much as possible. Women's experience of the war in these two industries was complex. The study explores how women could both challenge and reaffirm ideas about gender; how attitudes towards and about women could change and remain the same; and how employee and employers alike strove to undermine and maintain the sexual division of labour and labour processes that were threatened by the entrance of large numbers of women into jobs defined as men's work. Women's participation both challenged and reinforced traditional notions about gender. Essentially, despite being successful ‘bankers’ women remained unsuitable for a career in banking. Similarly, regardless of their participation in munitions factories, metal shops remained no place for women. Quantitative, oral interview and qualitative sources including contemporary newspapers and magazines, were used. In particular, a great deal of the evidence was derived from several databases constructed for this project.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.027 | 0.008 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".