Bibliographic record
Abstract
It is almost axiomatic that one can use the past to imagine (and therefore potentially prefigure) the future, and the authors of both of the books under review do that for the future of museums.Robert R. Janes wants to do more than predict; he wants to push museums to do nothing less than help lead humanity toward ways of solving the world's biggest problems, central among them global warming, but also to invent alternatives to the ever-increasing reliance on models for civil society that are derived from radical and unsustainable ideologies of capitalism.Janes is an erstwhile archeologist who worked with Dene hunters in the boreal forest of Northern Canada, a place at once rugged and hostile yet congenial to humans if they form small highly flexible and cooperative bands.Janes was also the CEO of the Glenbow Museum in Calgary-one of Canada's ten largest-and he is currently a consultant and editor of Museum Management and Curatorship.Janes' vision for the future is utopian; his past is at times prehistoric.Calgary with its glitzy skyline and trendy neighborhoods is not a part of the story.The Dene and the environment to which they have adapted are central to it.Steve Conn has a less exalted vision for the future of museums, although he also has an agenda beyond mere forecast.To remake the future, he wants museums to remember their roles as producers of a certain kind of civility and to continue to focus on this core mission.Conn, a historian, studies cultural contexts of shifts in museum practices as revealed through the careers of founders and directors, and in the trajectories of exhibits at specific deftly sketched sites: The Franklin Institute in Philadelphia, the Freer Gallery in Washington DC, the Museum of Natural History in New York, the (now forgotten) Philadelphia Commercial Museum, and several others.His past extends fleetingly into the late-18th century, but dwells for the most part in the late-19th to the late-20th century.It is a past in which museums and cities grew up together.His future for museums requires the city, and assumes that some sort of symbiosis between the city and the museum will be salutary for both.The two men are, in short, very differently positioned and very different in their sensibilities.Janes works in museums; but he is relentlessly critical of them, attacking most of their current taken for granted practices.Conn works on museums; yet, unlike many of his peers who disparage the museum for that institution's role in producing what they often characterize as insidious justifications for the status quo, he clearly likes them, and takes pleasure in visiting
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".