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Record W4406174566 · doi:10.3390/h14010003

Thrown to the (Were)Wolves: Sisterhood, Vengeance, and Liberal Feminism in Maggie Tokuda-Hall and Lisa Sterle’s Squad

2025· article· en· W4406174566 on OpenAlex
Jessica Caravaggio

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueHumanities · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicTerrorism, Counterterrorism, and Political Violence
Canadian institutionsQueen's University
FundersQueen's University
KeywordsFeminismGender studiesSociologyArtMedia studies

Abstract

fetched live from OpenAlex

In Maggie Tokuda-Hall and Lisa Sterle’s graphic novel Squad, protagonist Becca and her new friends at Piedmont High are not human adolescents but a pack of werewolves who must kill to stay alive and select teenage boys—“the WORST ones” (70)—as their meal of choice. The power of the pack’s “monstrous” bodies is a dangerous privilege and responsibility that Squad suggests is often misused to victimize innocents. The book critiques individualistic Western/liberal feminism—an ideology also critiqued by contemporary feminist writers—that encourages women and girls to gain power for themselves and then use it to perpetuate hierarchies of domination. Through an analysis of the figure of the werewolf and fantasies of revenge, this article suggests that both Squad’s narrative and its comic images guide readers toward an understanding of how liberal feminist ideology impedes collective empowerment. This article ultimately argues that Squad can be wielded as a potential feminist consciousness-raising tool for teaching about the ethics of different feminist ideologies.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.031
GPT teacher head0.323
Teacher spread0.292 · 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