Reflective Interviews in Virtual Reality: From Intervention to <i>Intravention</i>
Bibliographic record
Abstract
Researchers increasingly acknowledge the importance of material and spatial elements in knowledge generation. Interview research incorporating spatial and material elements is often limited to what is available near the interview location. Virtual reality (VR) technology, with its unique combination of affordances such as immersion, visualization, and interaction, allows for the creation of virtual environments that are difficult to access or nonexistent in physical settings, providing novel multimodal stimuli for participant reflection. We draw on an exploratory VR intervention with graduate students reflecting on challenges during their thesis work, using the multi-space coaching protocol “Clean Networks” to discuss the potential of VR in reflective interviewing. We found that the virtual environment was supportive of participants’ reflection and sensemaking. Moreover, we found that knowledge emerged through participants’ entanglement with different virtual-material stimuli, which prompted us to rethink our intervention as an intravention , drawing on sociomateriality perspectives. We discuss the potential and challenges of using virtual reality technology to support participants’ reflection through the availability of multisensorial inputs and describe the process of emergent knowledge through sociomaterial entanglements. We contribute to a growing body of research on multimodal interventions in interviewing by illustrating the role of new technologies in advancing the potential of interview-based research.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Qualitative | medium |
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 | 0.035 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".