Bibliographic record
Abstract
Anonymous Writing, Personal Reading Loretta CzernisFor centuries kings, political theorists, generals and cardinals thought of nations and institutions as social and corporate bodies.In this age of biotechnology, bodies are imagined as commodities that can be fixed.But the kind of mending required in the Canadian case is not like fixing a broken arm.It is much more tenuous and involves bringing citizens together not otherwise in touch with each other, who lack a sense of nationalism.Loretta Czernis applies her sociological training in document analysis to study one government prescription for what ails Canadians.The Report of the Task Force on Canadian Unity rewrote Canada by reinventing patriotism, essentially inviting Canadians to imagine a new Canada.The Report itself is the product of what she calls the federal writing machine which exists to continually rewrite and thus reinvent Canada.Czernis's contextural reading of the Report occurs on two levels.Reading technically, she examines the Report's anonymous writing style that asks readers to imitate its own conclusions (be patriotic, buy a flag, shop at home).Gestural reading invites reading as performance.Canadians are invited to participate in reshaping Canada by reading Canada allegorically, as a social body, capable of changing its form.What a document may intend is not always the same as what is read into it.Mistakes can and do occur in the reading.Czernis suggests that these "mistakes" constitute a significant form of resistance to the anonymous writing machine.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.885 | 0.837 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".