VRCHIVE: experiences conducting an online workshop teaching intergenerational participants to create virtual reality films about their lives during the COVID pandemic
Bibliographic record
Abstract
The VRCHIVE workshop was a first-of-its-kind exploratory pilot initiative to examine the feasibility of running a remote, intergenerational Virtual Reality (VR) storytelling workshop through the Toronto Public Library. The workshop took place during the COVID-19 pandemic, which has highlighted a need to develop solutions to address the digital divide and consequent increased social isolation in older adults. The overall program goals were threefold: (1) to create a ‘VRCHIVE’ of 360° VR films that documented participants’ lives during the COVID-19 pandemic, (2) to explore the challenges and successes of the program in order to evaluate its effectiveness, and (3) to understand, broadly, the program’s impact on technology literacy, feelings of isolation, and familial relationship strengthening. Five pairs of grandparents and grandchildren (n = 10) engaged in four, one-hour long online sessions each week in November 2020. Feedback was collected through facilitator observations and weekly debriefing sessions (n = 4), as well as online participant surveys (n = 3) and phone interviews (n = 2) conducted upon program completion. All intergenerational pairs successfully completed a VR film. Post-workshop, participants reported feeling less isolated, more connected with other people, and more confident in learning to use innovative technology. A detailed description of the workshop is provided, along with a discussion on recommendations for future iterations of the program that may serve as a model for other locations that wish to implement similar programming. Overall, participants reported positive experiences, and there is an appetite to sustain and scale the program in the future.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".