Performing landscape : notes on site-specific work and artistic research (texts 2001-2011)
Bibliographic record
Abstract
APPENDIXES 1 See for instance the introductions to Episodi 2 (Arlander 2009 d) and Episodi 3 (Arlander 2011 a), 2 In the application dated 15.5.2000 the research project is called "Visuaalinen kokonaiskompositio tutkimuskohteena -ohjaustaiteen, skenografian ja valaistuksen yhteishanke" [Visual composition as research object -collaboration of directing, scenography Annette Arlander from a broader perspective, before I concentrate on more narrow topics in the coming chapters: How are landscapes lived on, in and through?How are landscape and environment revealed, imagined, experienced, contested, animated and represented by, in and through performance?How can performance inform, extend and enhance engagement with, and the interpretation and appreciation of, landscape and environment?How can performances illuminate, explicate and problematize the multiplicity of attachments, meanings and emotions that resonate within and from landscapes: visual, aural, tactile?What strategies and forms of performance-exposition does working with landscape as medium and scene of expression inspire and necessitate?What is the life of landscape and how is it performed?(Pearson 2010, 188.) 9 I have discussed Massey's ideas of space and place more extensively in "Performing Time Through Place", published in Riku Roihankorpi and Teemu Paavolainen (eds.)SPACE-EVENT-AGENCY-EXPERIENCE (Arlander 2012 a).The text is a development of a paper "Private performances in public space" presented at PSi #16, Performing Publics, in Toronto, 9-13.6.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.087 | 0.019 |
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".