Bibliographic record
Abstract
Future Now \n \nAesthetica Art Prize Anthology 2016. \n \nMy work on renamed lightships "Kurtz" was selected for inclusion in this publication. \n \nA celebration of innovation and originality, the Aesthetica Art Prize anthology compiles an international community of established and emerging artists. Entering its 9th year, the award and accompanying annual invites audiences to connect with some of today’s leading practitioners whose projects touch upon key cultural, social, political, environmental and economic themes within the categories of Photographic and Digital Art, Painting and Drawing, Three Dimensional Design and Sculpture, and Video, Installation and Performance. \n \nFrom ecological concerns to transitions in urbanisation and developments in technology, the 2016 edition provides a cohesive commentary on the way that we inhabit the Earth. Questions of material and personal value align with definitions of virtual and physical realms; depictions of natural landscapes defy tangible realities; and an awareness of time is envisaged in elements of light and sound. \n \nThis year’s dynamic collection, which unites 100 artists from countries worldwide including Austria, Canada, Germany, Israel, Japan, Mexico, Poland, South Africa, the UK and the USA, is an outstanding resource and prime example of the calibre of works submitted to the Prize. Ranging from site-specific installations to new experiments with light and digital technologies, the 2016 selection instils an enduring reflection on humankind’s role in the present moment.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.281 | 0.120 |
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".