Bibliographic record
Abstract
I taught the Task Force Report (also known as "Pepin- Robarts") when I was preparing a course on Canadian society as part of the summer curriculum at Algoma College in Sault Ste.Marie, Ontario.Presenting chapters from a research report on Canadian unity provided the students with some insight into one federalist version of Canada.More than this, reading the Report stimulated a great deal of heart-wrenching discussion about personal aspirations.Many of the students felt that there was a serious discrepancy between what they had been promised by the federal government and what they had actually received.One of these students was an Ojibwa woman.Two were steelworkers laid off by Algoma Steel after many years of committed service to the company.Two were homemakers.Along with three young full-time students, this group began a dialogue with me about Canada which was fuelled by our readings of the Task Force Report.I am deeply indebted to these students who inspired me to further this reading.I tend to read a text as though I were studying the weave of a fabric.I came to recognize this pattern by studying the work of Mary Ann Caws, who has written much about the texture of texts.Written textures are subject to "the inconstant and obsessive co-constructions of observers and texts, where a roughness and deformation signal the high points of interest." 1 The written can also be read as a potential cloth, with some threadlike remnants ready to be woven, which will in turn inspire the spinning of new threads as writing.A reader weaves colourful but unfashioned threads.All readings are situated in time, in space, and in particular con-Notes to the Preface are on p. 113.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.454 | 0.259 |
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".