Her-Story: Her Voice in Franco-American History
Bibliographic record
Abstract
Her-story is real Franco-American women talking about real issues as they learned to navigate a man’s world. Women for the first time were entering the workforce to help support their families. Utilizing primary sources, such as oral histories, this thesis provides a first-hand look at four women who vary in age, have different family situations, different education levels, as well as different social situations, and gives us a snapshot into their early lives. These four women have graciously provided details, which bring us back to a time when mills and factories flourished as they ran 24/7 and owners got rich off the backs of the workers, paying them a pittance for too many hours of work in poor conditions. For many, their strength lay in their family and their faith, thus making religion a very important part of this paper as well. Her-story is meant to answer the question, who are the women who worked in the mills? For many women, this was their first time working outside the home, as coming from the farm, their work was at home raising the children and taking care of the home. Her-story gives them a chance to speak up with regards to a time in history which is little spoken of: a time of transition for French Canadians as they settled in New England towns and worked in mills and factories. Her-story examines the major facets of women’s lives: family, education, religion, community, and work. It scrutinizes them individually and collectively to give a picture of the emotional backdrop of an earlier time in American history, when discrimination against others was prevalent and territories such as “petit Canada” were safe places to be, a place where traditions and language were able to flourish until states put a ban on speaking French in school. As religion was also important, they even petitioned for a French-speaking priest so they could have their masses in their language. This thesis focuses on 1) “traditional” family: how that meant something different to each of the women interviewed, the size of traditional French families, and how that has changed; 2) education: who was able to be educated, parish school versus public school, and language utilized in the school; 3) community: petit Canada was a safe haven for many French Canadians with family nearby; 4) religion: keeping their faith and providing a place to practice their faith was important. Each of the women had a different perspective on how these items impacted their family and the overall effect on them personally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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.
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 teacher head, 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".