Bibliographic record
Abstract
Abstract This article describes a process initiated in 1983, at the Canadian Museum of Nature (CMN), which was based on the premise that a responsible natural history museum should assist society in shaping its collective future. The museum predicted that if it were able to help people understand themselves and their relationship to the natural world, the museum would again be seen as valuable to society, and thus would be supported in its efforts. The CMN therefore began to integrate its collections, scholarship, discovery, public programming, and public exhibits into broad, institution‐wide programs focused on the needs and interests of society. These programs enabled the museum to engage its visitors in “guided conversations” in which the museum provided the content, drawing on the research and communication strengths of the museum, while the audience, representing society, set the context. This guided conversation empowered the public to make informed decisions and to influence the museum and its work. CMN also designed exhibit formats that allowed the visiting public to contact industry and government decision‐makers with their opinions. The article describes the museum's evolution through several stages of increasing internal and external integration, ultimately using a managerial matrix to form project teams, with discipline‐based professionals focused on the interests and needs of society. Drawing on audience participation, the CMN reset its programming and offered advice and counsel to government and industry. The museum also took the first steps to include the traditional knowledge of indigenous peoples as an additional source of wisdom about the natural world. Financial and other support grew rapidly, effectively demonstrating a successful programmatic feedback loop helping society to shape its future using the museum as an information source and communication tool. The effort was terminated before the integration was completed, but nonetheless, CMN demonstrated that is possible to achieve a programmatic feedback loop that includes collections, science, exhibits, the general public and both government and industry decision‐makers.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.034 | 0.074 |
| Scholarly communication | 0.030 | 0.018 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".