“They seem to know the story better than I do myself”: The Portrayal of Florence Lassandro in Canadian Popular Culture
Bibliographic record
Abstract
In 1922, a court sentenced Florence Lassandro and Emilio Picariello to death for the murder of Alberta Provincial Police (APP) Constable Stephen Lawson in Coleman, Alberta. She was the only woman hanged in the history of the province. This thesis examines the relationship between Florence Lassandro and her representation in Canadian popular culture from 1922 to the present. Many historical works have sensationalized her role in the murder. By placing cultural productions in historical context, this thesis identifies and analyzes important social, cultural, and political moments in Canada’s history to argue that they have driven and altered the image of Lassandro in popular culture considerably more than the facts of the crime. The lack of her own voice has allowed people to mould her persona to fit their agenda. During her trial, newspapers reinforced nativist beliefs. Next, Phillip Godsell used her story to justify the internment of Italian-Canadian citizens in WWII because the public feared they would rise up against the Canadian government. In the 1970s and 1980s, the federal government’s introduction of multiculturalism as an official policy and an increased emphasis on women’s history influenced authors to frame her as an innocent victim. Most recently people have used her story to attract cultural tourists to southern Alberta. Nevertheless, her voice is lost in all reinterpretations of her life.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.058 | 0.026 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".