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Record W2728662714

Therapeutic Soul Searching

2017· dissertation· en· W2728662714 on OpenAlexaboutno aff
Brendan Michael Starling

Bibliographic record

VenueNational University System Repository (National University System) · 2017
Typedissertation
Languageen
FieldNeuroscience
TopicSleep and Wakefulness Research
Canadian institutionsnot available
Fundersnot available
KeywordsSoulPsychologyInformation retrievalComputer sciencePhilosophyEpistemology
DOInot available

Abstract

fetched live from OpenAlex

Personal identity is an important theme that often comes up in therapeutic endeavours, especially in societies that are saturated with free market capitalism and mass media culture. I begin the thesis by describing a foundational ethos that guides my practice as a counsellor. I then describe a process of therapeutic engagement that I call therapeutic soul searching and suggest how it can be applied to working with the theme of personal identity. For this purpose, I outline the ideas that have inspired my views on the craft of counselling. These ideas come from a variety of voices from differing fields, including the practice of counselling, and the world of art and literature. I explain how our modern-day culture industry impacts stories of personal identity. After outlining my foundational beliefs, I then provide an overview of some therapeutic possibilities that have been inspired by the narrative metaphor tradition and spiritual-based therapeutic traditions. I explain why these two therapeutic traditions work well with struggles relating to personal identity. Lastly, I suggest that further research and literature needs to focus on how personal identity differs across Canada's multicultural spectrum.

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)0.006
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0160.020
Scholarly communication0.0110.008
Open science0.0020.013
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0190.005

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 head0.044
GPT teacher head0.280
Teacher spread0.236 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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