Sites of Cultural Production in Response to Mass Extinction
Bibliographic record
Abstract
This conversation, mediated by Tara Nicholson, considers Stephanie Turner and EvaMarie Lindahl’s research in cultural representations of extinction and investigations of more-than-human forms of storytelling through an art historical lens. In response to Lori Gruen’s classification, extinction is a distinctive loss of ‘animal cultures’. It is more than biodiversity destruction or a static inventory of a species’ death. Nonhuman ways of building bonds, reproducing, teaching offspring, constructing homes and mourning the dead, are all systems of knowledge lost in extinction (Gruen et al. 2017). This conversation offers compassionate ways of bearing witness to species destruction and a space for empathy and kinship. The authors ask, how can dialogue between science and art lead to new understandings of the ‘wicked problem’ of mass extinction during climate crisis? Examining methodologies of cross-disciplinary storytelling and cultural production, this exchange connects museum practice, large-scale public artworks and artistic research as types of embodied knowledge to promote public awareness surrounding the acceleration of species extinction.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.019 | 0.041 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.005 | 0.006 |
| 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".