Bibliographic record
Abstract
"TRAINING FOR THE UNIVERSAL MUSEUM" addresses a theme of our time. A Canadian, Marshall McLuhan, coined the phrase "global village" for this age which has witnessed mass travel, mass communications, even mass credit. Are we now about to see the "mass museum", a museum presumably homogenized and popularized for whatever constitutes the greatest cohort of global visitor which might arrive on the doorsteps of every-museum, every-where? The contributors to this volume think not. But there is in these papers some evidence of worry that we as individuals and institutions responsible for the education and professional development of museum workers are failing to consider seriously the impacts of the "global" forces at work in modern societies. Angelica Ruge discusses how the Germans are re-organizing museum training into a cohesive scheme, searching out the best elements from the former two states that now comprise the new German state. Margaret Greeves and Chris Newbery document the British search for a value free (and universally applicable?) set of museological skills which will underpin performance standards in the workplace. Both of these papers offer a response to the redefinition of the post-modern national state which as we watch, is redrawing political boundaries on every continent, and emphasizing the portability of skills and learning for the itinerant knowledge-industry worker.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.077 | 0.009 |
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".