Bibliographic record
Abstract
y great thanks go to my good friend Sohan Singh Pooni for reading this manuscript in his always informed and attentive way, and for passing on documents that he collected and translated -the results of his years of searching for material for his recent book on Gadar heroes, Canada de Gadri Yodhe, a work that his fellow Punjabis have welcomed with spontaneous, enthusiastic, and deserved applause.My former student Archana Verma added greatly to my understanding of the Paldi/Mayo Siding community with interviews that she conducted twenty years ago in India and in Canada for her book The Making of Little Punjab in Canada.One of the people who helped her at that time was a member of the Mayo family, Joan Mayo, who was then looking for someone to write about Mayo Singh and Paldi, and who finally took on the job herself, producing a most readable and informative account.It was after meeting Joan Mayo that I had a chance to talk with Mayo Singh's son-in-law Joe Soroya, who subsequently gave the Simon Fraser University Archives his personal papers and photographs.Some of what he shared has found its way into this book.Also, as I worked my way through my filing cabinet of material acquired over many years, I was repeatedly thankful to Sarj Singh Jagpal, not just for his invaluable Becoming Canadians, based on the stories of his father's generation of Sikh immigrants, but also for some of the documents he retrieved from the closets and cupboards of people he interviewed, documents that he long ago generously helped me to photocopy.In the past few years, I've enjoyed the very special experience of spending time with Durai Pal
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.293 | 0.157 |
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".