Fine Art Insurance Policies and Risk Perceptions: The Case of Malta
Bibliographic record
Abstract
The aim of this paper is to identify the risks that need to be addressed when holding fine art, determine which are perceived as being the most important, and whether the risk perception is influenced by demographic variables such as age, educational background, and field of occupation. To identify the risks and evaluate the risk perception, we used a purposely designed questionnaire and sent it via various sources of communication systems and applications to individuals knowledgeable on fine arts. Findings revealed that, generally, art deterioration, art fraud, and art theft are the three main highlighted risks, with art deterioration considered in the high-risk range. In terms of risk perception, forgery is the biggest concern. On the other hand, considerations of the investment value of art lessened perceived risk exposure. Furthermore, the study has shown that certain risk perceptions were influenced by the participants’ demographic variables. Both the identified risks and risk perception considerations analyzed within this study provide us with insights as to what needs to be considered when offering fine art insurance, particularly when it comes to which risks that are perceived as being the most pressing by potential policyholders, and how these perceptions vary according to individual demographics variables as noted above.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".