Bibliographic record
Abstract
Playing the Mountain is an artistic research project investigating balance as the dynamic interplay of yinyang, through the practice of taijiquan (a Chinese internal martial art). Based on this embodied practice, I explore balance not as a state but as movement, by transposing this dynamic of opposing forces into a constellation of participatory, sculptural and expanded cinema forms. Drawing on principles of Chinese aesthetics from a diasporic perspective, Playing the Mountain deploys artistic strategies to consider agency, (non-)presence, tension, and resistance. This constellation traces unseen forces through kites, music, geological processes and Chinese calligraphy, gathering different ways to ask: what are the implications of understanding balance, not as a state, but as a process? This research project manifests through material investigations, martial arts practice, participatory exchanges and collaboration, as part of my broader PhD-in-Practice research project, undertaken at the Academy of Fine Arts Vienna. The exhibition and writing workshop were presented in Summer - Autumn 2022 at Centre[3] for Artistic + Social Practice, in Hamilton, Canada, curated by Lesley Loksi Chan; the kite-making workshop was conducted in Summer 2024 at Decentric Circles Assembly in Warsaw, Poland (various sites), curated by the Work Hard! Play Hard! working group. /* rules to make button only show up in META */.download-accessible { display:none;}.meta-right-col .download-accessible { display: inline-block; padding: 9px; margin-bottom:25px; border: 1px solid black; background-color:white;} Download Accessible PDF keywords: artistic research, embodied knowledge, martial arts, yinyang, cosmology, participation
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.083 | 0.012 |
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".