Bibliographic record
Abstract
Aside from unique architecture Science World has become iconic for families in Vancouver to explore hands-on exhibits and galleries that nurture their process of discovery and inspire connection with their natural, physical, and built environments. Our value of inquiry-rich, play-based, cross-disciplinary learning is embedded in every aspect of design, from fun interactive exhibits, engaging stage shows, and unique school programs. Throughout Science World you will discover that each gallery focuses on different themes and topics. Gallery spaces have their own narratives, learning goals, and outcomes. One of newest galleries at Science World is our Tinkering Space: The WorkSafeBC Gallery. The Tinkering Space has daily tinkering programming where you can solve problems, make new things from existing parts, create something cool and imaginative, and learn through experimenting and making mistakes. You’ll also learn about the science behind safety and how important it is to choose the right tools for the job. This informal learning environment captures the playful spirit of Science World all in one place. Creating the Tinkering Space is an iterative journey, and we continue to work hard to build out the visitor experience and pedagogical practice we have today.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".