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Record W3110440277 · doi:10.4324/9781351260800-7

Beyond the hikikomori spectrum

2020· book-chapter· en· W3110440277 on OpenAlexaboutno aff
Nicolas Tajan

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicYouth Substance Use and School Attendance
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

In the seventh chapter, the globalization of social isolation is detailed, arguing that hikikomori is not limited to Japan and not solely related to Japanese culture. Second, a brief overview of French hikikomori studies is provided, including accounts by a mother and a 25-year-old male. Third, a case study of a binational hikikomori , Misaki, and her parents, is presented using the most recent and stringent methodological tools of transcultural research: the Cultural Formulation Interview-informant version as well as the McGill Illness Narrative Interview. Recommendations and ways of rethinking social isolation are proposed. Overall, hikikomori resists medicalization and does not meet coherent and consistent criteria for being described as a spectrum in the psychiatric sense (i.e., Hikikomori Spectrum Disorder). Rather, it should be seen as a psychosocial condition. The chapter concludes with 20 lessons on hikikomori.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.030
GPT teacher head0.262
Teacher spread0.233 · 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 designQualitative
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

Citations2
Published2020
Admission routes1
Has abstractyes

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