Bibliographic record
Abstract
The quest for self-knowledge has been a guiding principle throughout history.Plato acknowledged the duality of self-knowledge as both individual (the Delphic maxim "Know thyself") and societal."[I]f a Canadian is to seek self-knowledge that is essential for both health and wisdom, he [sic] must have access to a wider self-knowledge of his historical community and its contemporary circumstances" (Symons 1975:14).Thus began the Canadianization project which saw Canadian artists in all fields recognized; Canadian subject matter and data taught in universities, colleges, and public schools; Canadians hired as faculty at our universities; and Canadian Studies programs flourish.Census data and census making are key means by which we know ourselves as Canadians, both at present and from whence we came in families and collectively.The Census is a unique way of knowing ourselves since it enables collection of data on everyone from the most disadvantaged and hidden members of society to the best known individuals.The Census is the preeminent text for us all, particularly those who are silent or weak, to make claims for recognition.The Census is also an increasingly utilized resource for tracing ancestry, to know ourselves as descendents.In this paper, we rely on Plato's duality of self-knowledge to explore some examples of the making of claims for recognition by groups past and present that may be lost with the cancellation of the mandatory long-form Census for 2011.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.064 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".