From Corporate Artification to Artification in the Third Sector
Bibliographic record
Abstract
ABSTRACT Artification refers to the process by which objects, practices, or entities not traditionally considered art are transformed into socially accepted art forms. A common example is graffiti, which was once regarded as vandalism but has since evolved into a recognized and celebrated form of art, but organizations and brands can also engage in artification strategies. This special issue of the Journal of Philanthropy and Marketing explores the concept of artification, with a particular focus on its application within the third sector. The six papers in this issue examine how artification fosters creativity, innovation, and social impact in non‐profits. Through case studies and empirical research, the issue demonstrates how third‐sector organizations, such as arts and culture institutions, charities, and foundations, can leverage artification not only to support the arts but also to enhance their legitimacy, build stronger community relationships, and increase credibility with stakeholders. The special issue examines studies on artification in both non‐profit organizations and corporate initiatives, emphasizing how art fosters social sustainability through creative partnerships. Collectively, these papers underscore the transformative potential of artification in the third sector, offering valuable insights for non‐profits seeking to integrate art into their strategic initiatives and enhance their social impact.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".