Bibliographic record
Abstract
Author of the Reality Bubble | Award Winning Broadcaster\nAward-winning host Ziya Tong has been sharing her passion for science, nature and technology for almost two decades. Best known as the co-host of Daily Planet, Discovery Canada’s flagship science program, she brings a wealth of knowledge, experience, and enthusiasm to the stage. Tong speaks on leadership, how to shift perspective, and the role of science and technology in society in her riveting and eye-opening talks.\nBefore co-hosting Daily Planet, Tong served as host and field producer for PBS' national primetime series, Wired Science, produced in conjunction with Wired magazine. In Canada, Tong hosted CBC's Emmy-nominated series ZeD, a pioneer of open source television, for which she was nominated for a Gemini Viewer's Choice Award. Tong also served as host, writer, and director for the Canadian science series, The Leading Edge and as a correspondent for NOVA scienceNOW alongside Neil deGrasse Tyson on PBS.\nIn the spring of 2019, she participated in CBC’s annual “battle of the books.” After a national four-day debate, she won Canada Reads.\nIn May 2019, Tong released her bestselling book The Reality Bubble. Called “ground-breaking” and “wonder-filled”, the book has been compared to The Matrix. It takes readers on a journey through the hidden things thatshape our lives in unexpected and sometimes dangerous ways.\nTong received her Masters degree in communications from McGill University, where she graduated on the Dean's Honour List. She currently serves on the Board of Directors of the World Wildlife Fund and is the founder of Black Sheep. Sheridan Creates I 2 KEYNOTE SPEAKER Author of | Award-Winning Broadcaster Ziya Tong The Reality Bubble
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.601 | 0.374 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".