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Record W4413911226 · doi:10.32920/ifmj.v5i1-2.2385

Playful Knowledge Transfer as a Response to Crises

2025· article· en· W4413911226 on OpenAlexvenueno aff
Jasmin Kermanchi

Bibliographic record

VenueInteractive Film and Media Journal · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Critical Thinking Development
Canadian institutionsnot available
Fundersnot available
KeywordsTransfer (computing)PsychologyComputer scienceParallel computing

Abstract

fetched live from OpenAlex

How can interactive practices of audiovisual knowledge transfer react to crises such as war and disinformation? How can we intervene in the chaos? What possibilities are there to increase mental resistance to manipulation? This contribution hypothesizes that playful knowledge transfer in interactive documentaries can respond to crises by making complex interrelationships experienceable and enabling users to try out actions in media environments that prepare them for challenges in real life. “You will soon join training on disinformation. You won’t be forced to do anything against your beliefs, so choose your character and decide which side you want to fight for. Trolls or Elves?” These are the opening words of the gamified interactive web-documentary Trolls vs Elves (UK 2023, Aleksandra Rydzkowska), which deals with the activities of disinformation-spreading internet trolls on the one hand and cyber activists called ‘cyber elves’ on the other. Game elements are used to explain real issues such as the disinformation narratives about the war in Ukraine. This contribution analyzes Trolls vs Elves drawing on research on gamification in documentary and journalism (Ferrer-Conill 2016). Gamification is understood as “the use of game design elements in non-game contexts” (Deterding et al. 2011, 2). Rather than presenting prefabricated knowledge, users experience it interactively. Methodologically, the study examines the strategies for making mechanisms experienceable in the web-documentary by analyzing the interaction possibilities on two levels (cf. Kermanchi 2019). The first level relates to the replication of processes in the larger structure. The navigation concept and its representation via the interface play a central role at this level. The second level focuses on the transformation of social action into intramedial action. Drawing on Bruno Latour, social action is not limited to human actors (Latour 2005, 71). For this examination of the individual interaction possibilities, the form, context, and purpose need to be examined (cf. Nash 2012, 200–201). In Trolls vs Elves, gamification serves both to impart knowledge and to intervene, as this contribution aims to show. By being ‘trained’ either as trolls or as cyber activists in the web-documentary, the players experience the mechanisms underlying the spread of disinformation narratives about the war in Ukraine. They learn how to debunk and counter disinformation techniques, and how to combat online propaganda. To progress in the online experience, users must decide, for example, which social media posts embedded in the project are spreading false information about Ukraine. By ensuring that all content in the web-documentary refers to reality and by revealing false information, the project counteracts existing disinformation narratives. Using documentary film material, network analyses, and data visualizations, Trolls vs Elves not only represents reality but also intervenes in it, as the contribution argues. The reference to reality, furthermore, becomes apparent when the project ultimately asks players not to follow the trolls’ advice learned during the web-documentary and not to spread disinformation in real life. By enabling intramedial action that is transferable to real life, gamification therefore has potential as well as limitations and dangers, which this contribution examines.

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 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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.845
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.398
Teacher spread0.371 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations0
Published2025
Admission routes1
Has abstractyes

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