Euphoria & Dystopia:The Banff New Media Institute Dialogues
Bibliographic record
Abstract
Featuring worldwide pioneers, cultural icons, and digital-industry innovators, Euphoria & Dystopia documents the key decade of the Banff New Media Institute, between 1995 and 2005. Attracted by innovative programming set against Banff's extraordinary mountain landscape, hundreds of international practitioners were brought together to debate the new forms wrought by technology. Their discussions, gathered here, celebrate the belief that the creative sector, in collaboration with science and engineering, can invent technologies that work for human betterment and create a more participatory culture. This volume charts the rise of the emerging field of digital media art and industry. It is essential reading for scholars, developers, and theorists of new media. The contents of Euphoria & Dystopia include over 150 original transcripts drawn from thousands of hours of audio material and organized by themes that range from data visualization to the interface of technology and the body to curating to gaming. It includes fully annotated references and biographies of participants, a detailed list of events that took place at Banff between 1995 and 2005, and transcripts from the annual Interactive Screen event and numerous summits and workshops. The book includes essays from the co-editors and from leading new-media theorists including Allucquere Rosanne Stone, N. Katherine Hayles, Steve Dietz, and Eric Kluitenberg. Included is the catalogue of the exhibition The Art Formerly Known As New Media (2005) and a DVD of HorizonZero, the groundbreaking bilingual electronic journal produced at Banff from 2002 to 2004.
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 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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.023 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".