Museum and university mutations: the relationship between museum practices and museum studies in the era of interdisciplinarity, professionalisation, globalisation and new technologies
Bibliographic record
Abstract
The universe of the museum is in the process of profound transformation, a reflection of the societies in which museum institutions evolve. The number of museums has grown considerably and their activities have diversified. Our traditional understanding of this sector is no longer adapted to the present-day context and many are attempting to redefine it. The same holds true for the teaching of museum studies, since the milieu has been professionalised and has taken on new responsibilities. This article tracks recent developments in museum studies and invites the reader to reflect on current trends towards increasing the autonomy of the museum, in light of the fact that the museum has become an object of study. Limiting the discussion to a specific aspect of the museum, the author takes stock of the contribution made to the field by various disciplines. She also evaluates the museum's role and function in terms of eight meta-functions. There are increasing expectations of museums: they must reflect and attempt to make sense of society, resolve social problems and provide new orientations, serve as a lever for minorities and open a window onto other cultures. The challenges facing museums also affect the curriculum of museum studies programmes. Are we teaching in order to reproduce the status quo, or in order to effect change?
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 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".