Bibliographic record
Abstract
This book is a testimony to my love of photographs and stems from countless hours poring over family albums with my grandmother.It also reflects the support and caring of many others who have influenced my life and my academic endeavours.Writing can be a lonely and isolating experience, and it is often not achievable without personal, practical, and financial support.My sincere gratitude goes to my father; my daughter, Dylan; my brother, Steve; my sister-in-law Sil; and my uncle and aunt, Lennart and Linda Mannik, for their assistance.I would also like to thank all my friends (including my canine and feline companions) for their love, support, and patience over the last few years.Researching this project was a rich and enjoyable experience.I owe an immense debt of gratitude to all of the Walnut's surviving passengers who met with me and often welcomed me into their homes.This project would not exist except for your gracious gift of time, stories, memories, and photographs.Every person who was interviewed provided interesting, engaging, and heartfelt insights into their experiences as newcomers to Canada in 1948.I would particularly like to thank the photographers whose creative work makes up the collection at the centre of this book: Max Kalm, Manivald Sein, Enno Lauri, and Joann Saarniit
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 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 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".