Bibliographic record
Abstract
m a n y people deserv e r ecogn i t ion for the encouragement and insight they lent to this project at various times over the last six years.By introducing me to early American and Indigenous literatures, Drew Lopenzina put me on this path, fundamentally changing the way I think about Canada, Atlantic Canada, and the northeast.April Shemak supported me through my own messy experience of "cartographic dissonance," which played out in what was otherwise perhaps the most unsuitable environment possible: the small east-Texas prison town of Huntsville.Jennifer Andrews humoured and supported me from the beginning of our relationship, encouraging me to pursue my interests with impunity while simultaneously (and sagely) advising me to use fewer soil metaphors in my grant proposals.Thank you, Jen, for always looking out for me and for showing me that it is possible to balance this kind of all-consuming work with a robust family life.Elizabeth Mancke has made innumerable contributions to everything I have published since meeting her; without our weekly coffee dates at the library, this book would not be near finished.Elizabeth, thank you for showing me how to map ideas out loud and on paper.Your brilliance, kindness, and generosity make the scholars around you immeasurably better, and I aspire to someday mentor and support others in a similar way.
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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 | 0.004 | 0.022 |
| 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 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.274 | 0.166 |
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".