Landscapes, Memory, Monuments, and Commemoration: Putting Identity in Its Place
Bibliographic record
Abstract
The central theme of this paper is the invention of Canada through strategies that have attempted to integrate a people separated by geography, history, ethnicity, and class by constructing a national identity that is self-consciously aware of place. Nationalizing-states have long made use of many devices and agencies to create an emotional bonding with particular histories and geographies. In particular, many new nations have had to confront the problems of incorporating peripheral domains and assimilating diverse peoples into the body-politic, and establishing a degree of national homogeneity—or at least a semblance of common central purpose. Technological, constitutional, institutional, and cultural mechanisms have been directed to the mission of establishing a monolithic identity and integrating peripheral loyalties. Others have attempted to nurture an identification with place and community through a variety of mediums: high art and literature; mass communications and kitsch; architecture and
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.040 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".