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Record W1999363768 · doi:10.1300/j017v19n02_08

We'd Like to Ask You Some Questions, But We Have to Find You First: An Internet-Based Study of Lesbian Clients in Therapy with Lesbian Feminist Therapists

2002· article· en· W1999363768 on OpenAlexaff
Georgia K. Quartaro, Terry E. Spier

Bibliographic record

VenueJournal of Technology in Human Services · 2002
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsGeorge Brown College
Fundersnot available
KeywordsLesbianAsk priceThe InternetPsychologyPsychoanalysisWorld Wide WebComputer scienceBusiness

Abstract

fetched live from OpenAlex

SUMMARY This paper explores some issues related to an Internet-based study dealing with lesbian clients' perceptions of their lesbian feminist therapists. A 60-item questionnaire was posted on a Web site so respondents could complete it online, submitting answers anonymously through a forwarding service. Respondents were recruited through postings to 20 listservs that focus on gay/lesbian/bisexual issues or the psychology of women. Data collection proceeded rapidly, with 182 responses within seven weeks. Results indicated that the therapist's sexual and philosophical orientation was important to the client, but that the clients tended to make assumptions about the latter. Specific activities typical of feminist therapy were often missing or were not recollected by clients. The advantages of using the Internet to draw a wide range of respondents is set against the problems of generalizability, the difficulty in communicating directly with respondents, and the sample bias inevitable in using self-identified volunteers who have Internet access.

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)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0100.002
Scholarly communication0.0050.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.001

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 head0.042
GPT teacher head0.362
Teacher spread0.320 · 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 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

Citations3
Published2002
Admission routes1
Has abstractyes

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