Looking Reality in the Eye: Museums and Social Responsibility (Janes and Conaty, eds.)
Bibliographic record
Abstract
The idea for the book, Looking Reality in the Eye: Museums and Social Responsibility, emerged from a panel discussion on museums and social responsibility at the annual meeting of the Canadian Museum Association in 2002.The themes of the different chapters presented look closely at social responsibility and use specific examples of museums connecting with communities to examine a variety of issues that affect our everyday lives.These include such topics as the social and natural environment, crime, economic inequality, and political issues such as the repatriation of human skeletal remains to native peoples.A question that runs through this book is how museums can continue to be relevant and sustain themselves when they are challenged by the perils of the global marketplace, declining attendance, reduced public funding, and earned revenues.The examples offered demonstrate that there are museums moving beyond their preoccupation with the bottom line and that are embracing activities that address the "troublesome aspects of our contemporary world that help to make sense of this emerging search for significance in the museum world" (p.3).Many of the authors suggest that museums have two choices: (1) they can stay where they are and maintain status quo in their mission of collecting, preserving, and caring for the collections and quite possibly become irrelevant, or (2) they can connect with their surrounding communities, and possibly world communities depending on their mission, and address the many issues and choices that humans are faced with on a daily basis.Instead of the exhibitions and educational programming being determined by the collections and developing stories around them, these stories are based on connections with the communities and focus on topics that pertain to social responsibility internally and externally using the collections.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".