Post Atomic: A conversation between Alison Sperling and Anna Volkmar with a visual response by Donald Weber
Bibliographic record
Abstract
From 2005 until 2007, Canadian photographer Donald Weber spent time around the Chernobyl region in Ukraine, documenting the area and its inhabitants. The result of this is the photographic series Post Atomic (2005-2007). On a late November afternoon, Alison Sperling, a post-doctoral fellow at the ICI (Institute for Cultural Inquiry) in Berlin, and Anna Volkmar, a PhD candidate at Leiden University, engaged in a conversation on these photographs, which functioned as an impulse for a broader reflection on the nuclear era and its conceptual challenges. Faced with a phenomenon that resists comprehension, Sperling and Volkmar deal with ways of challenging popular framings of the nuclear, especially in the so-called ‘exclusion zones’, through different theoretical tools and objects of study. What kind of artistic practices can unshroud the complexity usually associated with the nuclear in popular imaginaries? What potential does the (non-)human body hold to broach the disturbing effects of the nuclear legacy? How can we face and responsibly address the ethical stakes that come with living in and with the nuclear, more than 30 years after the Chernobyl disaster?
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".