Going down like a song: national identity, global commerce and the Great Canadian Party
Bibliographic record
Abstract
On 1 July 1992 over 100,000 people assembled in various locations across Canada to see their favourite bands play live at the Great Canadian Party. Broadcast on television and radio, this Canada Day spectacle celebrated the country's 125th birthday, but rather than being organised by the state or a non-profit making citizen's movement, it was facilitated by more than $100,000 of corporate sponsorship. Drawing on fieldwork in Vancouver, I will argue that external funding initially helped Canadian musicians but soon allowed outside sponsors to control the live music industry. These sponsors could then co-opt anxieties about national unity in a selective celebration designed purely for their own ends. By addressing the Great Canadian Party's emergence and historic moment the following discussion will explore what it meant for Canada to be represented through a giant commercial, a commercial drawing on shared national identity in order to sell the products of a global industry. The Party revealed how judiciously agents of commerce could use popular culture to negotiate between geographic scales. Despite that success, however, the resistance of participating bands suggests that the Party could not secure full hegemony for its sponsor's project.
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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 | 0.003 |
| Science and technology studies | 0.032 | 0.019 |
| Scholarly communication | 0.011 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".