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Record W4319975833 · doi:10.1145/3531210.3531211

Surveying the Effects of Remote Communication & Collaboration Practices on Game Developers Amid a Pandemic

2022· article· en· W4319975833 on OpenAlexaff
Elizabeth Caravella, Rich Shivener, Nanditha Narayanamoorthy

Bibliographic record

VenueCommunication Design Quarterly · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsYork University
Fundersnot available
KeywordsPandemicGeneral partnershipWork (physics)ProductivityProcess (computing)BusinessCoronavirus disease 2019 (COVID-19)Public relationsComputer scienceEngineeringPolitical scienceEconomic growthEconomicsMedicine

Abstract

fetched live from OpenAlex

Communication and collaboration are essential parts of the game development process. However, during the global pandemic, the shift to remote work marked a sudden change in how developers could communicate and collaborate with one another, as usual ad-hoc conversations that happen in physical offices were nonexistent. Based on a partnership grant study with the International Game Developers Association (IGDA), this piece focuses on the results of a survey that examined developers' mental health and productivity during the COVID-19 pandemic. Our findings suggest that most game developers want a hybrid or fully remote position even after pandemic conditions subside. Failure to address the pandemic's impact on the game development industry risks ignoring a rich area of technical communication complicated by, and responsive to, hybrid workplaces.

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)0.007
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.081
GPT teacher head0.359
Teacher spread0.278 · 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 designObservational
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

Citations4
Published2022
Admission routes1
Has abstractyes

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