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Record W4394059162 · doi:10.5281/zenodo.7565915

Ressources - Video games as a tool for ecological learning : the case of Animal Crossing - COROLLER & FLINOIS - 2023

2023· dataset· en· W4394059162 on OpenAlexaffabout
Simon Coroller-Chouraki, C. Flinois

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2023
Typedataset
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité de Sherbrooke
Fundersnot available
KeywordsEcologyComputer scienceGeographyCommunicationZoologyEnvironmental scienceBiologyPsychology

Abstract

fetched live from OpenAlex

The present repository includes : Survey answers - 200 people, anonymized. Quizz about animals and vegetals that are and are not present in the game ANIMAL CROSSING NEW HORIZONS. Personal questions (age,, gender, location). Self assesment of "Naturalistic Fiber". At the end, question about the video game Animal CROSSING, and then free space Translated R code, based on the present dataset. Runs figures and tests used in our paper "Video games as a tool for ecological learning : the case of "Animal Crossing : New Horizons" during Covid-19 quarantine" The survey itself is available (in french unfortunately) at the following link : https://forms.gle/GgwULMcg8KnBqDt26, if the link is broken, please don't hesitate to contact me Nintendo Game Content guide at the following link : https://www.nintendo.co.jp/networkservice_guideline/en/index.html Appendix (S1 : table of raw datas ; S2 : Normal and QQ plots) Approval of Ethical Research Committee of Université de Sherbrooke (Canada, QC) " [...] furthermore, after reviewing the application for review, no ethical issues were identified by the committee.The committee notes that:The data were collected from a population of individuals who do not a priori present the characteristics of a vulnerable population; The risks associated with participation in the research are minimal; The data collected are anonymous; Individuals have been informed that the data may be used for scientific purposes; However, we remind you that in the future, any research project, as defined in the policy, must be approved by the research ethics committee before proceeding with the collection of data. Therefore, please accept this letter in lieu of an ethics certificate from the Research Ethics Board - Education and Social Sciences of the Université de Sherbrooke. This letter may be used when submitting for publication or presentation of the results of this study or for any results of this study or for any other request related to the ethical approval of this project. " Mme Ariane Tessier Coordonnatrice à l'éthique de la recherche - Université de Sherbrooke, CA QC If you have any problem with the present ressources, or if you want to work and publish works containing these datas, please contact me at : simoncoroller.biologie@gmail.com CC : Simon.Coroller@usherbrooke.ca I would gladly discuss with you ! Best wishes. COROLLER & FLINOIS

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)0.003
metaresearch head score (Gemma)0.010
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: Dataset · Consensus signal: Dataset
Teacher disagreement score0.091
Threshold uncertainty score0.304

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0910.052

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 head0.073
GPT teacher head0.353
Teacher spread0.280 · 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
GenreDataset

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
Published2023
Admission routes2
Has abstractyes

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