Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dan Burgar is a cheerleader and innovator who has helped Vancouver become a global powerhouse in VR/AR/Metaverse. Dan is the co-founder and CEO of the Frontier Collective, a coalition of leaders in tech, culture, and community driving forward the development and support of Web3, the metaverse, VR/AR, climate tech, AI, and eSports.Furthermore, Dan serves as the president of the Vancouver VR/AR Association, an organization with a mission to make Vancouver/BC a world leading hub for growing and scaling VR/AR companies Nominated as one of 2021's LinkedIn Top Voices: Technology & Innovation, BIV's Top Forty Under 40, and named as a BC500 leader, Dan has written for and offered tech insights to publications including TechCrunch, Betakit, VR Scout, DailyHive, and BC Business. Dan has curated events and spoken at venues around the globe including Web Summit, SXSW, and the TED Conference. Dan also advises and mentors many fast-growing startups and entrepreneurs with an emphasis on diversity.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.707 | 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 it