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Record W2507613439

Self-Mapping in Counselling: Using Memetic Maps to Enhance Client Reflectivity and Therapeutic Efficacy

2016· article· en· W2507613439 on OpenAlexaffvenue
Lloyd Hawkeye Robertson

Bibliographic record

VenueCanadian Journal of Counselling and Psychotherapy · 2016
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsAthabasca University
Fundersnot available
KeywordsTransformative learningPerspective (graphical)SituatedConstruct (python library)PsychologyEmpowermentSelfUnpackingCognitionComputer sciencePsychotherapistSocial psychologySociologyPedagogyArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Combining theory and practice, this article demonstrates how the construct of the self may be represented graphically with implications for our understanding of self-determination and counselling. It begins with a review of attempted graphic self-representations in psychology, social work, and education. The self is then situated ontologically within the perspective of cultural evolution, and this paradigm is used to inform the construction of maps consisting of units of culture called memes . Graphic self-maps of two individuals, one in counselling and one not, are compared and contrasted. The self-maps depict self-defining cognitive structures combined with psychological and environmental determinants. It is proposed that such graphic illustrations could benefit counsellors and their clients in planning and executing transformative change. Further research is recommended that explores the effect of self-mapping on client empowerment, the structure of client selves, and the use of mapped cognitive pathways in treatment.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.882
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.040
GPT teacher head0.361
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations4
Published2016
Admission routes2
Has abstractyes

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