Evergreen Avengers: Nature and Kaijū in the Twenty-First Century
Bibliographic record
Abstract
After a decade of dormancy following the release of Tōhō Studios’ Godzilla: Final Wars (2004), Godzilla and other kaijū burst back onto the scene with Legendary Pictures’ Godzilla (2014). Several American sequels and a television series set in Legendary’s MonsterVerse quickly followed over the next ten years. Meanwhile, Japan’s Tōhō used their radioactive creation’s global success to reignite their own films with Shin Godzilla (2016), an animated trilogy, and Godzilla Minus One (2023). Short-format media like Chibi Godzilla and Godziban also circulated thanks to streaming services. Similarly, Godzilla’s longtime competitor Gamera also emerged from hibernation in an animated series produced by Kadokawa Corporation, Gamera Rebirth (2023). But how do these new installations relate to or depart from their predecessors’ predilection to address environmental concerns? This article continues the ecocritical analysis of kaijū eiga, expanding it to the 2010s and 2020s, as a coda to our duograph Japan’s Green Monsters (2018). This article picks up where we left off, examining the recent releases from an ecocritical standpoint. This analysis reveals that today’s films remain steeped in environmental commentary, but both fragmented and updated for the new concerns of the twenty-first century.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".