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Record W4220685412 · doi:10.3390/rel13040275

Replika: Spiritual Enhancement Technology?

2022· article· en· W4220685412 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReligions · 2022
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsQueen's University
Fundersnot available
KeywordsMeaning (existential)Spiritual HealthPsychologyIntervention (counseling)SpiritualitySpiritual practiceRelation (database)Engineering ethicsSociologyPsychotherapistComputer scienceMedicineEngineeringAlternative medicineClinical psychology

Abstract

fetched live from OpenAlex

The potential spiritual impacts of AI are an under-researched ethics concern. In this theoretical essay, I introduce the established spiritual assessment tool, the Spiritual Assessment and Intervention Model (Spiritual AIM). Next, I discuss some existing and probable AI technologies, such as immersive tech and bots that have impacts on spiritual health, including the chat-bot Replika. The three core spiritual needs outlined in the Spiritual AIM are then engaged in relation to Replika—(1) meaning and direction, (2) self-worth/belonging to community, and (3) to love and be loved/reconciliation. These core spiritual needs are used to explore the potential impacts of the chat-bot Replika on human spiritual needs. I conclude that Replika may be helpful only as a supplement to address some spiritual needs but only if this chat-bot is not used to replace human contact and spiritual expertise.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.026
GPT teacher head0.343
Teacher spread0.317 · 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