Evaluating Standards for Private-Sector Financial Institutions and the Management of Cultural Heritage
Bibliographic record
Abstract
ABSTRACT Financial institutions typically avoid projects that will have a significant adverse effect on cultural heritage because it creates unwelcome risk and can affect their reputation. For bank clients, adverse project effects on cultural heritage can result in reputation risk, impede access to finance and insurance, increase operational costs, and jeopardize on-time and on-budget delivery of projects. To address this risk, financial institutions implement environmental and social policy frameworks that include specific requirements for the consideration of cultural heritage. This article examines the place of cultural heritage in the lending practices of 25 of the world's largest private-sector banks and its relevance for heritage practitioners who may be retained to provide advice, review or undertake fieldwork, and prepare studies in keeping with the private-sector bank policies and external standards described. The article concludes with a recommended best practice for private-sector financial institutions, a call to action for heritage practitioners to advocate for robust safeguards, and a call for support of the UN's Sustainable Development Goals by both heritage practitioners and private-sector financial institutions.
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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 | 0.167 | 0.310 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".