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Record W4367834402 · doi:10.1002/ajcp.12678

Identifying abolitionist alignments in community psychology: A path toward transformation

2023· article· en· W4367834402 on OpenAlexafffund
Andrea L. DaViera, Caroline Bailey, Davielle Lakind, Natalie Kivell, Fitsum Areguy, Kymberly Byrd

Bibliographic record

VenueAmerican Journal of Community Psychology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsWilfrid Laurier University
FundersSociety for Community Research and ActionPartenariat Canadien Contre Le Cancer
KeywordsCommunity psychologyEmpowermentSociologyField (mathematics)IdeologyBeneficenceSocial psychologyEpistemologyLawPsychologyPolitical scienceAutonomy

Abstract

fetched live from OpenAlex

Psychology is grounded in the ethical principles of beneficence and nonmaleficence, that is, "do no harm." Yet many have argued that psychology as a field is attached to carceral systems and ideologies that uphold the prison industrial complex (PIC), including the field of community psychology (CP). There have been recent calls in other areas of psychology to transform the discipline into an abolitionist social science, but this discourse is nascent in CP. This paper uses the semantic device of "algorithms" (e.g., conventions to guide thinking and decision-making) to identify the areas of alignment and misalignment between abolition and CP in the service of moving us toward greater alignment. The authors propose that many in CP are already oriented to abolition because of our values and theories of empowerment, promotion, and systems change; our areas of misalignment between abolition and CP hold the potential to evolve. We conclude with proposing implications for the field of CP, including commitments to the belief that (1) the PIC cannot be reformed, and (2) abolition must be aligned with other transnational liberation efforts (e.g., decolonization).

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)0.010
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.690
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.007
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 head0.208
GPT teacher head0.543
Teacher spread0.336 · 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 designObservational
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

Citations6
Published2023
Admission routes2
Has abstractyes

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