Bibliographic record
Abstract
Contemporary climate change research today speculates that life as we know it is at an end (Scranton, 2015). As planetary conditions optimal to the survival of the human species are undergoing profound transformation, the question of what future awaits the human species has become both prominent and pervasive. Extending into the speculative art of video games, this post-apocalyptic mis-en-scene today constitutes something of a familiar reference point for gamers, who might find in such popular games as Left 4 Dead (2008) and Gears of War (2006) a particular speculation on survival where life as we know it encounters the destructive forces of nuclear devastation, epidemic, invasion, or any one of a myriad catastrophic scenarios now cliché in the medium. Yet, the ways that video games think survival nevertheless constitutes a speculative fulcrum on which is dramatized both “world without-us”, or rather, an impersonal hostile world unremitting to the desires of ‘man’, and the human that might survive it (Thacker, 2011). Significant amongst such speculative games are the massive post-apocalyptic worlds of Fallout 3, Fallout: New Vegas, and Fallout 4, each of which evokes the question of how we might survive after nuclear catastrophe and its transformation of the planet into a foreboding ecology populated by mutated animals, radioactive dead-zones, loosely organized bandit hordes, and nomads foraging the resource scarce post-apocalyptic future.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.026 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".