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Record W2810298897 · doi:10.1057/978-1-349-95816-0_1

Introduction to Global Psychologies: Mental Health and the Global South

2018· book-chapter· en· W2810298897 on OpenAlexaff
Suman Fernando, Roy Moodley

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2018
Typebook-chapter
Languageen
FieldPsychology
TopicAcademic and Historical Perspectives in Psychology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMental healthDiversity (politics)Global mental healthField (mathematics)PsychologySociologyPsychiatryAnthropology

Abstract

fetched live from OpenAlex

The training of professionals, working in the mental health field, is deficient because of the Eurocentric nature of psychology and psychiatry that dominates it. The chapter discusses knowledge that is relevant and meaningful as contributions to the mental health field in a world that is increasingly globalized and /or composed of communities with diversity of cultural forms. Since the chapter serves as an introduction to the main body of the book, it describes the division of the book into four parts and discusses briefly the content of each chapter. Finally in concluding, the chapter discusses the future place of global psychologies and looks to a future when a diversity of psychologies informs the training of professionals working in the field of mental health. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.001
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: Review · Consensus signal: none
Teacher disagreement score0.043
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.003
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0430.013

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 head0.023
GPT teacher head0.322
Teacher spread0.299 · 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
GenreReview

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

Citations4
Published2018
Admission routes1
Has abstractyes

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