Opening remarks, Neal Overstrom
Bibliographic record
Abstract
Neal Overstrom is a biologist, designer, educator and former Director of the Nature Lab at Rhode Island School of Design. His professional work has focused on promoting environmental literacy through informal learning experiences. Prior to coming to RISD he held senior posts for exhibit development, research and zoological management at the Mystic Aquarium in Mystic, Connecticut and was a Design Associate for Kent + Frost Landscape Architecture. Conducting field studies in regions ranging from sub-Arctic Hudson’s Bay, Canada to the Gulf of California, Mexico, Neal has authored or co-authored scientific publications on topics ranging from shark development and reproduction to marine mammal biology and behavior. He also served as project director for a major public aquarium expansion featuring innovative fish, bird, and marine mammal habitats with more than two million gallons of re-circulating seawater systems and a multimedia ocean education center. Neal earned a Bachelor of Science degree in biology from the University of Connecticut, a Master of Arts in zoology from Connecticut College, and a Master of Landscape Architecture from the University of Massachusetts, Amherst. In 2009 he was named the University of Massachusetts Olmsted Scholar, exploring the intersection of living systems, technology, and aesthetics in designing for sustainability. His interests involve investigating the biological influences on design, particularly the ways in which pattern, form and living elements in the built environment can reinforce our human-nature connection.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".