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Record W2796622167 · doi:10.1177/0739891318759725

<i>That Dragon, Cancer</i> Goes to Seminary

2018· article· en· W2796622167 on OpenAlexaff
John W. Auxier

Bibliographic record

VenueChristian Education Journal Research on Educational Ministry · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicTheological Perspectives and Practices
Canadian institutionsTrinity Western UniversityWestern University
Fundersnot available
KeywordsVideo gameGraduate studentsSociologyPsychologyMedia studiesMedicineMedical educationMultimediaComputer science

Abstract

fetched live from OpenAlex

That Dragon, Cancer is a “serious” video game that has garnered wide attention in the gaming community and popular press. The game was created by a team of independent game designers led by Ryan and Amy Green as a way of sharing their family’s journey of caring for their son Joel, who had been diagnosed with pediatric cancer. This article describes the use of the game within a graduate course on pastoral counseling and reflects upon student reactions as an example of the potential usefulness of serious games in theological education.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.002
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0480.013

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 head0.183
GPT teacher head0.562
Teacher spread0.379 · 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 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

Citations10
Published2018
Admission routes1
Has abstractyes

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