Bibliographic record
Abstract
‘#HASHTAG: Visions of Epigenetics’ is a series of multi-media works, events and an associated publication by Harrison, developed during his time as resident artist for EpiGeneSys European Network of Excellence, an EU-funded FP7 initiative, (2010-2016). Harrison acted as principal director, editor and curator of the overall arts research project, in addition to producing his own artwork. Building on Harrison’s research exploring the visualisation of Epigenetics and Systems Biology, he utilised the metaphor of C.H. Waddington’s ‘Epigenetic Landscapes’ (1957) as a model for how gene regulation modulates development. Epigenetics is an expanding field of biomedical research relating to changes in gene expression, phenotype and heredity factors as a result of external reasons. Harrison collaborated with network scientists and other practitioners to develop original models of this concept. Pilot exhibitions were presented at multiple venues including the EINA University of Design & Art Gallery, Barcelona (2014). This research culminated in a large-scale exhibition held across the seven gallery spaces of the Cité International des Arts in Paris (May 2015). Selected elements have since been exhibited in international institutions such as The China Academy of Arts, Hangzhou; Isla Centre for Arts, University of Guam; George Segal Gallery, Montclair State University, USA; and Enterprise Square Gallery, Edmonton, Canada. The accompanying publication (re) visions is published by Discovery Press. Harrison’s research formed the basis for additional activities, leading to his invitation to be Co-Investigator (Arts & Public Engagement Projects Leader) on the Personal Genome Project (PGP UK) led by Professor Stephan Beck (UCL, London).
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.841 | 0.804 |
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".