MétaCan
Menu
Back to cohort
Record W2156794804

Pyrophilia: A Need for Further Discussion

2015· article· en· W2156794804 on OpenAlexaff
Jerrod Brown, Eric Hickey, Mario Hesse, Warren Maas, Dallas S. Drake, Michael Flanagan, Samantha Lee, Hannah Brown, Janae Olson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsConcordia University
Fundersnot available
KeywordsParaphiliaPsychologyHarmConversationIdentification (biology)Set (abstract data type)Sexual arousalSocial psychologyPsychotherapistArousalComputer scienceSexual behaviorCommunication
DOInot available

Abstract

fetched live from OpenAlex

Pyrophilia is an under researched form of paraphilia that involves heightened sexual arousal associated with fire or fire-setting (Newton, 2006; Hickey, 2015).  The potential for harm caused by a person compelled to set fires to satisfy sexual cravings is theoretically greater than other forms of paraphilia.  Therefore, more research on the identification, diagnosis, and treatment of pyrophilia may have significant societal benefits.  The purpose of this article is to initiate a conversation on the topic of pyrophilia.  This article presents previously discussed parameters of pyrophilia through a synthesis of peer-reviewed, clinical research publications.  Moreover, case studies further explored and highlighted common elements and critiques of pyrophilia.  Finally, the need for future research is discussed.  Defining the term pyrophilia and correctly conceptualizing it as a syndrome, diagnosis, or subtype of another diagnosis will aid the development of best practices for assessment and treatment.

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)0.067
metaresearch head score (Gemma)0.127
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.067
Threshold uncertainty score0.353

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0670.127
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.004
Bibliometrics0.0050.005
Science and technology studies0.0090.021
Scholarly communication0.0160.046
Open science0.0080.009
Research integrity0.0240.038
Insufficient payload (model declined to judge)0.0130.003

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 head0.081
GPT teacher head0.378
Teacher spread0.297 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same topicSexuality, Behavior, and TechnologyFrench-language works237,207