Bibliographic record
Abstract
Jody Berland’s North of Empire is an astute, compelling retrospective of half a career’s thought on media spaces from a distinctly Canadian perspective.It is the best book in a generation to argue for the value of a Canadian approach to cultural studies, not just parochially but as a critical contribution to the contemporary study of culture anywhere. Given its substantive focus, it should be no surprise to learn that the Canadian Communication Association awarded North of Empire its 2009 Gertrude J. Robinson book prize (full disclosure: I served on the jury). Nonetheless, an anthology of previously published essays is not an obvious choice for a book award.Referring in her postscript to the heightened concern about protecting and nurturing Canadian culture amidst debates over free trade negotiations in the 1980s, Berland reminds us that “just because something is past does not mean it is not present” (p. 301). She could be explaining the continuing relevance, even spectral centrality, of her own concerns in this collection of essays. Although the chapters were originally published between 1988 and 2005, each is updated and thematically positioned out of chronological order to create a surprisingly cohesive monograph.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".