Bibliographic record
Abstract
Mel Alexenberg's Educating Artists for the Future Mel Alexenberg (Ed.). (2008). Educating Artists for the Future: Learning at the Intersections of Art, Science, Technology and Culture. Bristol, UK: Intellect Books/ Chicago: University of Chicago Press. 344 pages. ISBN 978-1-84150-191-8 (hard cover)Educating Artists for the Future: Learning at the Intersections of Art, Science, Technology and Culture is a rare find. Editor Mel Alexenberg has done a remarkable job of bringing together outstanding artist/educators who are grappling with issues related to technology, ecology, creativity, agency, identity, and community. Each individual author provides rich written descriptions of projects they have undertaken, the conceptual underpinnings that frame their work, and the implications of their practices for art education in informal and formal learning contexts. I am certain that readers reviewing this book will feel a profound sense of collectivity knowing we are at the edge of transforming the world in which we live.The volume is divided into the following five sections, book-ended with an introduction and epilogue by the editor: Beyond the Digital, Networked Times, Polycultural Perspectives, Reflective Inquiry, and Emergent Praxis. Each section has four chapters making this 22-chapter book an extensive array of ideas from authors representing Brazil, Canada, China, Czech Republic, Finland, Germany, India, Israel, South Korea, Switzerland, Turkey, the United Kingdom, and the United States. Its international character alone makes this book a must read for educators wanting to understand the arts and education at a global level.One of the most prominent threads running throughout the book (for me) is its references to complexity theory. A number of authors refer to complexity theory being an influential theory coming from the mathematical sciences, while other authors may not refer to complexity theory but rather, the language of the chapter reiterates the characteristics of the theory. One of my colleagues, who coincidentally is a mathematics educator, has joined forces with two curriculum scholars to write several books on complexity theory in education (e.g., Davis, Sumara & Luce-Kapler, 2007). I was delighted to see how artists, scientists, and educators have taken up this theory in such strong yet innovative ways and can hardly wait to introduce this book to the many complexity theorists I know working in education, and particularly art education. It is on this basis that I would highly recommend the book to undergraduate and graduate students as well as to instructors who want to re-imagine how we perceive and understand education, art, science, technology, and culture now and in the future.Why complexity theory? Everywhere we look today, we are networked in decentralized (and centralized) structures and through a variety of interactions (with lots of feedback loops) taking place within these structures, creating selforganizing communities or projects. Complexity theory, as developed by several authors in this book, discusses four characteristics of complex systems: differentiation, interaction, self-organization, and emergent behavior. For artists and art educators, differentiation is about how we use materials in a variety of ways and how our connections with people can be accessed differently from what may have been expected. Interaction refers to the direct relationships viewers and authences have with people and processes that, in turn, provide opportunities for participants to alter the artworks or what is learned. Selforganization applied to the arts would suggest that while artists may start an artistic project, others would also participate in the development of the process and/or product, and as such, the project would be attributed to all of the creators. Emergent behavior is very exciting for artists and educators. Here is where a complex system naturally evolves and adapts into a number of possible structures, perhaps even simultaneously, knowing that the end product may look incredibly different from the original proposition. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".