Bibliographic record
Abstract
Examining various cultural products-music, cartoons, travel guides, ideographic treaties, film, and especially the literary arts-the contributors of these thirteen essays invite readers to conceptualize citizenship as a narrative construct, both in Canada and beyond. Focusing on indigenous and diasporic works, along with mass media depictions of Indigenous and diasporic peoples, this collection problematizes the juridical, political, and cultural ideal of universal citizenship. Readers are asked to envision the nation-state as a product of constant tension between coercive practices of exclusion and assimilation. Narratives of Citizenship is a vital contribution to the growing scholarship on narrative, nationalism, and globalization. Contributors: David Chariandy, Lily Cho, Daniel Coleman, Jennifer Bowering Delisle, Aloys N.M. Fleischmann, Sydney Iaukea, Marco Katz, Lindy Ledohowski, Cody McCarroll, Carmen Robertson, Laura Schechter, Paul Ugor, Nancy Van Styvendale, Dorothy Woodman, and Robert Zacharias.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".