Collaboration in Visual Culture Learning Communities: Towards a Synergy of Individual and Collective Creative Practice
Bibliographic record
Abstract
Abstract A visual culture learning community (VCLC) is an adolescent or young adult group engaged in expression and creation outside of formal institutions and without adult supervision. In the framework of an international, comparative research project executed between 2010 and 2014, members of a variety of eight self‐initiated visual culture groups ranging from manga and cosplay through contemporary art forms, fanart video, graffiti and cosplay in five urban areas (Amsterdam, Budapest, Chicago, Helsinki and Hong Kong) were studied through interview, participant observation and analysis of art works. In this article, collaborative group practices and processes in informal learning environments are presented through results of on‐site observations, interviews and analyses of creations. VCLCs are identified as inspiring, collaborative spaces of peer mentoring that enhance both visual skills and self‐esteem. Authors reveal how identity formation is interrelated with networking and knowledge sharing. Adolescents and young adults become participants of global communities of their creative genres through reinterpretation and individualisation of shared visual repertoires. In conclusion, implications for art education from the VCLC model for creative collaboration are suggested.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.001 | 0.015 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".