Mapping the Canadian landscape : the performing arts and experiential perspectives
Bibliographic record
Abstract
Focusing on a specific time period in Canadian performing art history--from the 1970s through to the late 1990s--this thesis "maps out" three artists' experiences in the landscape and the way these experiences are represented to an audience through performance. Using specific examples from the repertoire of Davida Monk, Paul Thompson, and R. Murray Schafer, I make a case for considering these performing artists as landscape researchers. I suggest that their performances explicitly and implicitly explore foundational questions about the meanings, uses, and affective power of landscape in ways that are analogous to the writings of cultural geographers during the same period. Like Yi-Fu Tuan, John Jakle, Denis Cosgrove and Jay Appleton, these performing artists examine the experience of humans in the landscape and focus on issues of place and space, homeland, and the meaning of landscape. Monk, Thompson and Schafer extend the perspectives of the geographers and bridge important gaps in their ways of knowing landscape.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.037 | 0.033 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.002 | 0.006 |
| 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".