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Record W3017780189

Waking Up the Dissident: Transforming Lives (and Society) with Feminist Counseling

2020· article· en· W3017780189 on OpenAlexaboutno aff
Donna F. Johnson

Bibliographic record

VenueJournal of international women's studies · 2020
Typearticle
Languageen
FieldPsychology
TopicCounseling Practices and Supervision
Canadian institutionsnot available
Fundersnot available
KeywordsGender studiesSociologyPsychoanalysisPsychotherapistPolitical sciencePsychology
DOInot available

Abstract

fetched live from OpenAlex

When I was a student in the 70’s I took a year off to travel the world with a friend. Despite taking every precaution, I was sexually assaulted twice. The incidents changed the course of my life. I completed my studies and began working in a refuge for battered women. There I bore witness, not only to unimaginable cruelty, but to widespread institutional indifference to women’s suffering. Decades later, police, judicial and child welfare responses remain inadequate in Canada (as everywhere), and mental health practitioners continue to routinely blame and pathologize women. As a counselor, first at the shelter, later in a police crisis unit, I struggled to know how to respond when women sought my guidance. Should they report being beaten, raped, threatened with death? Should they seek treatment for depression? Could they lose their children? Could they be charged for defending themselves against their batterers? Women were looking for reassurances that I couldn’t give. What I could give them was tools to understand the forces acting upon their lives. I began to incorporate a feminist analysis into my work, including a sociology lesson and consciousness-raising in every session. I started bringing women together in groups, where many problems considered personal and psychological were recognized as common and social, requiring political solutions. For many women, reflecting on their problems from a feminist perspective was truly liberating and empowering.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.168
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.039
GPT teacher head0.343
Teacher spread0.304 · 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.

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

Citations1
Published2020
Admission routes1
Has abstractyes

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