From Cultural Tolerance to Mutual Cultural Respect: An Asian Artist’s Perspective on Virtual World Cultural Appropriation
Bibliographic record
Abstract
Culture is to be lived and to be learned. The connotation of cultural symbols is negotiated and learned within a culture (Sturken & Cartwright, 2004). When we are part of the dominant culture using another’s cultural objects, we may not know the context of that object, which may lead to cultural appropriation. In this paper, I review appropriation through three different domains: appropriation in art, appropriation in media and technology, and appropriation in cultural studies. I specifically chose to use denotation and connotation as the means to analyze interview text and visual data because these two coding systems are able to draw the cultural meanings embedded in the texts beyond the surface level of understanding. Findings can be categorized into three threads: 1) cultural appropriation in virtual worlds, 2) caring about cultural appropriation, and 3) solutions to cultural appropriation in virtual worlds. This research suggests that cultural exchange and mutual respect are the solutions to cultural appropriation in virtual worlds. Visual literacy will help virtual world residents learn how to read, see, decode, and create virtual imagery.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.046 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".