Bibliographic record
Abstract
In an era where digital landscapes bleed into people’s lives, the convergence of narrative videogames and personality tests stands as an intriguing yet overlooked frontier. This presentation delves into this intersection, drawing inspiration from the thought-provoking Black Mirror episode "Hang the DJ." It unfolds in a world where a dating system dictates the course of romantic relationships. Participants use a digital assistant to navigate a series of predetermined relationships, with the ultimate goal of finding their perfect match. The episode's exploration of how technology influences and, at times, dictates personal connections provides a thematic backdrop for our investigation. Our journey then ventures into “Swipe Night: Killer Weekend”, a narrative experience within the match-making app Tinder. Unlike "Hang the DJ", “Swipe Night: Killer Weekend” introduces users to a branching narrative structure, allowing them to make choices and influence the plot at critical junctures – and the potential matches. For a brief period, Tinder transformed into a dynamic storytelling platform, reshaping the way individuals connect in the digital realm: it challenged traditional paradigms, introducing a brand-new way to know more about a potential match’s personality based on their choices during the game. Our exploration further extends to “FREERIDE”, a distinctive videogame contributing to the understanding of the intersection between gaming narratives and personality profiling. It transcends conventional gaming experiences by seamlessly blending open-world exploration with personality-driven decision-making and Daniel Vella’s concept of ludic subjectivity. Players traverse a richly detailed virtual environment while their choices subtly shape the unfolding narrative – and giving them a final judgement based on their decisions. Are you more of an explorer? Are you sociable? This game gives you actual scores after calculating how you controlled your avatar during your run. As this presentation connects the dots between these three titles, it also aims to shed light on the consequences of integrating branching narratives' elements and personality tests into digital platforms. It unveils a captivating landscape where pixels and personalities intertwine. From the speculative future of dating apps inspired by Black Mirror's conceptual groundwork to the real-world experiments of “Swipe Night: Killer Weekend” and the immersive gameplay of “FREERIDE”, this presentation invites participants to contemplate the evolving relationship between digital narratives and the understanding of human personalities in our interconnected age. The synthesis of storytelling and user-driven choices not only redefines traditional gaming experiences; it also offers a glimpse into the transformative potential of interactive narratives in shaping the ways in which we humans connect, engage, and know ourselves.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.143 | 0.020 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".