Bibliographic record
Abstract
Lingering holds the weight of what was, embodying presence within absence and exerting influence through traces that waft in the crevices of memory, the weight of the air in a room, or a persistent scent. It is the quiet after-effect, a trace that subtly endures in sensory details. Set against the captivating pull of forward-looking, futuristic narratives as a form of resistance by imagining the potentiality of the otherwise, lingering offers an alternative mode of engagement—embracing presence within absence, the reverberations of what has been, and the slow, deliberate unfolding of meaning in the present. Focusing on the interactive film In the Realm of Ripley (2024), set in a futuristic 2080 Korea, this paper reframes lingering as a powerful, understudied force that reshapes our understanding of temporality as mediated by immersive technology and the increasingly blurred boundaries between truth and fiction. Its central premise—a detective navigating a fragmented memory system—plunges the audience into an environment where the unreliability of perception and the enduring echoes of unresolved past events are palpable. By integrating interactive elements, such as collective decision-making and personalized VR experiences, the film extends the concept of lingering beyond its narrative, embedding it into the audience’s experience. This paper argues that In the Realm of Ripley not only reflects the fragility of memory but also uses the immersive medium to embody lingering as a thematic and sensory state of a fragmented present. Analyzing the film’s use of visual, auditory, and haptic cues to sustain a lingering affect, this paper will draws on theories of temporality and affect, particularly those articulated by Henri Bergson and Sarah Ahmed, to position lingering as a mode of engagement that resists linear progress and fixed narratives. Bergson’s notion of duration provides a foundation for understanding the slowness and multiplicity of temporal experience that lingering embodies, while Ahmed’s work on affect frames lingering as a felt resonance that shapes individual and collective memory. Additionally, the paper engages with Lev Manovich’s conceptualization of new media temporality and the immersive potentials of virtual reality as explored by Janet Murray, to interrogate the role of interactive technology in mediating lingering.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".