Good governance principles for the cultural heritage sector: lessons from international experience
Bibliographic record
Abstract
Abstract Purpose – The collapse of some prominent corporations over the last ten years has been attributed to poor governance. Not‐for‐profit agencies are now examining their own governance policies and practices in an attempt to prevent the calamities that have plagued the private sector. Because heritage sites, conservation organizations and heritage‐based tourism are significant factors in the social life and economies of many countries, the proper management of cultural heritage initiatives is vital. This paper seeks to undertake the development of a set of good governance principles applicable to the oversight and operation of cultural heritage institutions. Design/methodology/approach – The fifth World Parks Congress, in South Africa in 2003, encouraged the development of governance principles for protected areas based on the UNDP document Governance for Sustainable Human Development. Using these standards as a reference for the cultural heritage setting, UNESCO and ICOMOS charters and conventions, along with documents from National Trusts in specific countries are examined with regard to their relevance to good governance. Findings – A set of good governance criteria and principles including legitimacy and voice, direction, performance, accountability, and fairness, is developed. Practical implications – The paper addresses governance issues and principles relevant to non‐governmental and public sector governance in the cultural heritage sector. Originality/value – The paper draws on principles of good governance from several international heritage related agencies, trusts and organizations to develop a set of principles that can be recommended for use in the cultural heritage sector.
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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".