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Record W7117666544 · doi:10.3390/h15010007

Grant Allen’s Folk Horror Mediation of the Science and Spiritualist Debate

2025· article· en· W7117666544 on OpenAlexaff
Ian M. Clark, Brooke Cameron

Bibliographic record

VenueHumanities · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicReligious Studies and Spiritual Practices
Canadian institutionsQueen's University
Fundersnot available
KeywordsWonderAllegoryForegroundingMaterialismMythologyFolkloreSimulacrumPoliticsNarrativeMedievalismCommodification

Abstract

fetched live from OpenAlex

This essay reads Grant Allen’s “Pallinghurst Barrow” as folk horror about the late-Victorian spiritualist debates. We read Allen’s story as not only sympathetic to spiritualism, but also as critical of the gendered and genred politics of fin-de-siècle scientific materialism which would preclude such occult experiences—or what we frame as feminine ways of knowing. In both form and content, “Pallinghurst Barrow” challenges masculine science by foregrounding the powerful influence (on Rudolph, the protagonist) of the Gothic ghost story (“gipsy” Rachel’s cautionary tale, repeated by young Joyce). Allen’s interest in the folkloric origins of religion can be traced back to Herbert Spencer’s “Ghost Theory,” a proto-sociological explanation for the cultural construction and transmission of myth (or spirits). A lifelong friend and devotee of Spencer, Allen employs his mentor’s sociology as a way to make sense of non-material forces, including the ghost story circle and its production of Gothic awe or wonder (the wonder tale). Ultimately, then, Allen’s infamous folk horror reads as an allegory of late-Victorian spiritualist debates and, more importantly, as a defence of feminine modes of knowledge and myth-making through collective story-telling.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0080.017
Scholarly communication0.0050.004
Open science0.0010.005
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0040.001

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 head0.045
GPT teacher head0.259
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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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