Exploding Empire: Post/Apocalyptic Representations 1979-2016
Bibliographic record
Abstract
Exploding Empire: Post|Apocalyptic Discourse 1979-2016, is a cross-medial, transnational study of apocalyptic and post-apocalyptic narrative representations in the United States, Canada, England, and Japan. In particular, this dissertation examines the antagonistic relationship such discourse has to empires and the history of imperialism. The vertical bar in “Post|Apocalyptic” indicates that the term refers to both apocalyptic and post-apocalyptic discourses. I chose that character to reinforce the significant conceptual overlap among different media forms (novels, films, video games) and sub-genres (post-apocalyptic, science fiction, horror). This dissertation responds to the common critique of post|apocalyptic discourse, posited by critics from Susan Sontag to Naomi Klein, that it primarily atrophies political activity and fails to provide meaningful social criticism. This dissertation argues that while some forms of post|apocalyptic discourse can reinforce hegemonic beliefs, post|apocalyptic narrative forms can also contribute to rational-critical debate within the public sphere and help foster awareness of global concerns, such as climate change. This dissertation focuses on Octavia Butler’s 1980s and 1990s prose fiction, Japanese animation during the 1990s, Cormac McCarthy’s The Road and Margaret Atwood’s MaddAddam trilogy, the trans-national films of Guillermo Del Toro and Alfonso Cuarón, and the game The Walking Dead from Telltale Studios.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".