Bibliographic record
Abstract
I would like to begin by expressing sincere gratitude to all of the Haliburton County cottagers who generously invited me to their cottages and shared their stories with me as I sought to understand what their cottages meant to them.Without their time, interest, hospitality, openness, and encouragement, this book would never have been possible.I must also recognize those cottagers from various corners of Ontario's cottage country who learned of my project and expressed sincere interest in it.To respect their privacy, I cannot mention any of these people by name but all of them deserve my thanks for their patience in waiting for the book to finally be published.I have also adjusted other details to further mask their identities. SvitlanaGouin (Pcholkina), one of my graduate students, was also my research assistant.She contributed significantly to the research here and offered me valuable insights into the curiosities of the cottage experience.I owe a particular debt of gratitude to Betsy Struthers, a good friend and a passionate lover of her cottage who has believed in my project since I first mentioned it.Thank you for the encouragement, thoughtful commentary, and tidbits of information for my research.Your vote of confidence has meant a great deal
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.259 | 0.175 |
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".