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Record W4407168270 · doi:10.5117/9789463725682_intro

Introduction

2025· book-chapter· en· W4407168270 on OpenAlexaff
Sarah Stang, Mikko Meriläinen, Joleen Blom, Lobna Hassan

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicGothic Literature and Media Analysis
Canadian institutionsBrock University
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Monsters have fascinated us since time immemorial, springing from our cultural imaginations and appearing in our stories, myths, legends, fairy tales, literature, artwork, poetry, comic books, films, television shows, and, of course, video games. Monstrous beings embody our fears, anxieties, and prejudices, while also being irresistible figures whose forms and stories are continuously re-envisioned, remediated, and even sometimes redeemed. The concept of monstrosity itself is a form of chimera, defying classification and categorization, morphing and mutating to fit all kinds of analytical moulds. This is perhaps because monsters themselves are ambiguous and often contradictory figures. As Rosi Braidotti (2011) has pointed out, monsters “represent the in-between, the mixed, the ambivalent as implied in the ancient Greek root of the word for ‘monsters’: teras, which means both horrible and wonderful, object of aberration and adoration, placed between the sacred and the profane” (p. 216, emphasis in original). In this sense, while the monster is a villain in countless stories and media, it is really an ambiguous figure, defying clear categorization as either “bad” or “good.” For example, in his book Monster Theory, Jeffrey Jerome Cohen (1996) discusses how the figure of the monster often symbolically polices the borders of what is permissible, signalling that to step outside of social norms risks either “attack by some monstrous border patrol or (worse) to become monstrous oneself” (p. 12). Yet, at the same time, some monsters can be guardians or protectors, such as the kaiju Mothra; they may also be objects of dangerous or transgressive romantic interest, such as vampires or sirens; or they can be cute companions, like Pokémon—short for “Pocket Monsters.” The monstrous also sometimes offers space for the subversion of hegemonic norms and power structures (see Wilson, 2020) and a point of identification for marginalized groups. For instance, feminist activists have reclaimed a figure like Medusa as a symbol of women’s rage against oppressive patriarchal systems, and even a pop star like Lady Gaga has used the term “monster” to refer to the queerness, deviance, and non-normativity of herself and her fans.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.579
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.000

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.

Opus teacher head0.009
GPT teacher head0.263
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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