Bibliographic record
Abstract
The Rezort (Steve Barker, 2015) tells the story of a post-zombie-apocalypse U.K. in which the remaining specimens of the zombie population are employed as attractions at “The Rezort,” a leisure island on which humans can have zombie-theme safari experiences, including remorseless shooting of the undead. The apparently formulaic narrative comes with an ambitious social commentary. The film directly links zombies, and their exploitative and persecutory treatment, to refugees, reflecting the worries of public opinion in the U.K. around the migration crisis at the time of its production. The article offers textual analysis inspired by the four levels of meaning (referential, explicit, implicit, symptomatic) found in David Bordwell’s Making Meaning (1989). The eye-match dynamics are analyzed as textual cues for the creation of the explicit meaning (zombies are people too), and the narrative turning point and holocaust references are interpreted as cues to the implicit meaning (the refugee crisis is like previous persecutions in history). Finally, cues in the film are interpreted as symptoms of the impending Brexit, the referendum for which would take place the year after the film’s release.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.009 |
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".