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Critical Psychology, Prevention, and Social Justice

2012· book-chapter· en· W211549482 on OpenAlexaff
Isaac Prilleltensky, Geoffrey Nelson

Bibliographic record

VenueOxford University Press eBooks · 2012
Typebook-chapter
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsCommunity psychologyHealth psychologyPsychological interventionPsychologyInjusticePositivismValue (mathematics)Social psychologyPsychosocialSociologyEpistemologyPsychotherapistMedicinePublic healthNursing

Abstract

fetched live from OpenAlex

Abstract Abstract Critical psychology and prevention have a goal in common: the promotion of individual and community well-being. Their ways of achieving it, however, vary. In this chapter we explore similarities and differences between these two disciplines in terms of values, ontology, epistemology, and practices. Whereas prevention has assumed a value-neutral, positivist and largely individualistic approach, critical psychology has endorsed an explicitly value-laden and social action orientation. The value of social justice, very prevalent in critical psychology, assumes a marginal position in prevention. With the notable exception of George Albee and his disciples, prevention professionals have embraced the promotion of health and the reduction of personal risk factors as their main goals. Whereas critical psychologists have done a great deal to show the role of injustice in mental health and psychosocial problems, they have not done as well as preventionists in systematically implementing and evaluating psychosocial interventions. We argue for a synergy between critical psychology and prevention to promote both sustainable well-being and social justice.

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 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 categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.952
Threshold uncertainty score1.000

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 studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.002
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 head0.128
GPT teacher head0.415
Teacher spread0.287 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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
Published2012
Admission routes1
Has abstractyes

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