Bibliographic record
Abstract
The year 2019 marks the fiftieth anniversary of the National Arts Centre. In this new and revised edition of Art and Politics, Sarah Jennings covers the highs and lows of Canada's most important national performing arts institution over the course of five decades, bringing the story up to the present. Art and Politics is a riveting tale of Canada's finest musicians, actors, and dancers and efforts to put their art at the forefront of both the national and the international scene. Through over 150 interviews with artists, top officials, senior politicians, and others who affected the fate of the National Arts Centre, the book recounts the organization's early years; the impact of government monies first lavished and then withdrawn, which resulted in its near collapse in the late 1990s; and how over the past two decades, its CEO, Peter Herrndorf, a gifted leader, has brought it back from the brink. The most recent transformations revealed by this new edition include the architectural makeover of the organization's brutalist-style building in Ottawa, responses to the changing cultural milieu in Canada, and the launch of a national Indigenous Theatre Department in the fall of 2019. Told through the voices of those who created the organization, Art and Politics affirms that the National Arts Centre embodies its motto: "Canada is our stage."
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.021 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.010 |
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".