Immersive Sampling: Exploring Sampling for Future Creative Practices in Media-Rich, Immersive Spaces
Bibliographic record
Abstract
Creative practitioners rely on sampling to understand, explore, and construct problems; or gather resources for later use. Despite practitioners’ ability to experience immersive environments, sampling from them remains limited to primarily visual captures (e.g., screenshots, videos), which overlook the richness and variety of available media. To address these challenges, we describe “Immersive Sampling” as a new way to frame information gathering in the context of immersive environments. In the context of Immersive Sampling, practitioners engage in experiencing immersive environments while capturing, organizing, revisiting, and remixing found content. We situate this subset of tasks in literature and argue for their importance for emerging, future content creation domains. To further explore how Immersive Sampling might take place, we created VRicolage, a proof-of-concept prototype showcasing a set of interactions in Virtual Reality to sample, revisit, and remix captures. Given the democratization of immersive environments, Immersive Sampling provides practitioners with a means to collect, revisit, and remix digital materials.
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 | 0.029 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".