Bibliographic record
Abstract
Abstract Who doesn’t love a good zombie splatter-fest? Appealing to commercial audiences and cultural theorists alike, the American zombie movie has been characterized as both a pariah of low art and a rich source of critical insight. In its earliest incarnations, along with much of the broader genre of horror film that preceded it, the zombie film was deemed unworthy of critical analysis. Pioneers such as George A. Romero, however, provided filmic fare that was imbued with political significance. As the zombie genre evolved and matured, it reflected increasingly sophisticated and radical interrogations against the hegemony of the patriarchal culture in which it was produced, carried through the metaphor of zombies who were subaltern in either their undead abjection or their disenfranchized social identities. Recently, however, Christopher Sharrett insisted that “[a]lthough the popularity of the zombie film today is enormous, its value as social/political commentary is not only almost totally gone, it has been transformed by neoconservative culture into its opposite.” This article seeks to elucidate the different interpretations of the somewhat nebulous term ‘subaltern’ and the way it has been co-opted by conservative factions through a thorough analysis of the 2009 reflexive zombie parody Zombieland.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".