Bibliographic record
Abstract
Ireland’s Great Famine produced Europe’s worst refugee crisis of the nineteenth century. More than 1.5 million people left Ireland, many ending up in Canada. Among the most vulnerable were nearly 1,700 orphaned children who now found themselves destitute in an unfamiliar place. The story Canada likes to tell is that these orphans were adopted by benevolent families and that they readily adapted to their new lives, but this happy ending is mostly a myth. In Finding Molly Johnson Mark McGowan traces what happened to these children. In the absence of state support, the Catholic and Protestant churches worked together to become the orphans’ principal caregivers. The children were gathered, fed, schooled, and placed in family homes in Saint John, Quebec, Montreal, Bytown, Kingston, and Toronto. Yet most were not considered members of their placement families, but rather sources of cheap labour. Many fled their placements, joining thousands of other Irish refugees on the Canadian frontier searching for work, extended family, and the opportunity to begin a new life. Finding Molly Johnson revisits an important chapter of the Irish emigrant experience, revealing that the story of Canada’s acceptance of the famine orphans is a product of national myth-making that obscures both the hardship the children endured and the agency they ultimately expressed.
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.050 | 0.013 |
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".