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Record W2885275146 · doi:10.1177/1555412018791697

Moving Beyond Churn: Barriers and Constraints to Playing a Social Network Game

2018· article· en· W2885275146 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueGames and Culture · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsnot available
FundersMitacs
KeywordsCasualAttritionPleasureIncentiveSocial network (sociolinguistics)Internet privacyComputer scienceAdvertisingPublic relationsPsychologySocial mediaBusinessWorld Wide WebMicroeconomicsPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Social network games (SNGs) are genre of casual games that require being logged into a social networking site (e.g., Facebook) to access the gameworld. To date, investigations of these games are typically focused on the rate of attrition or “churn,” reinforcing the idea that SNG players exist to make the developer money rather than participating in a game they derive pleasure from. Seeking to recenter the player in research about SNGs, this article reports on a survey of former players ( N = 147) who were queried about their reason(s) for no longer participating in YoWorld, a Facebook-based SNG. Findings indicate that players typically quit because of external constraints to their leisure time rather than no longer interested in the game, which are not barriers to play that can be overcome by personalized in-game incentives, the typical developer response to prevent churn from taking place.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.267
Teacher spread0.258 · 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