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Record W4398219756 · doi:10.24908/ohi.v2i1.17576

Developing a Visual Novel about Landmines: A One Health Approach

2024· article· en· W4398219756 on OpenAlex
Julian Jongkind, Nashmia Anwar

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

VenueOne Health Innovation · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsQueen's University
Fundersnot available
KeywordsComputer sciencePsychology

Abstract

fetched live from OpenAlex

Landmines pose a serious threat to humans, the environment, and non-human animals. They are a wicked problem, and no perfect solution exists due to how they can damage the environment, put human and non-human animal lives at risk, and how recurring war increases the number of landmines globally. Landmines impact communities across the world, and there are two major pathways for dealing with landmines: Demining and education. While the value of demining cannot be overstated, the action done here focuses on the education side, and bringing awareness of landmines to youth, who are one of the most vulnerable groups. We made a visual novel using Ren’Py software and published it on online platforms Steam and Itch.io to help it reach a global audience, though it was written with youth in mind. Our project aims to open public discourse around landmines and facilitate an appreciation for the effects they have beyond just their impact on humans, focusing on equality between the pillars of One Health.

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)0.009
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.700
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.006
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.451
GPT teacher head0.574
Teacher spread0.124 · 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