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Record W4206748168 · doi:10.1080/10447318.2021.2016237

Echoing the Gameplay: Analyzing Gameplay Sessions across Genres by Reconstructing Them from Recorded Data

2022· article· en· W4206748168 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInternational Journal of Human-Computer Interaction · 2022
Typearticle
Languageen
FieldComputer Science
TopicData Visualization and Analytics
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceHuman–computer interactionPsychology

Abstract

fetched live from OpenAlex

We present Echo+, an expanded and improved version of our previous gameplay tool, Echo. Echo+ is designed to help bridge the gap between video footage analysis and game analytics. It facilitates this by interactively reconstructing gameplay sessions from recorded data, complete with their original graphics. We present and discuss several new features and changes integrated into Echo+, many of which are based on feedback from our previous evaluation of Echo. We also present a user evaluation that compares the usefulness of Echo+ across four different popular game genres. Participants found Echo+ to be useful in all genres, but there was no clear ordering to them regarding usefulness. We identified several key differences in terms of how Echo+ is used in different genres. In addition, we discuss various avenues for expansion of the tool and possible approaches for evaluating it in the future.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.814
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0050.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.084
GPT teacher head0.395
Teacher spread0.311 · 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