Cakes and Ale, Paintings and Sculptures: Directors’ Duties and Corporate Art Collecting
Bibliographic record
Abstract
Corporations spend significant amounts of money on art collecting and art sponsorship, but little research has been done on the question of whether such activities are permissible in light of directors’ duties. This article addresses that issue by examining whether corporate expenditure on art collecting and sponsorship is consistent with the duty to act in the bests interests of a corporation, the duty to exercise powers for a proper purpose and the fiduciary duty not to make improper use corporate information or position. This is done first by examining the scale of corporate expenditure on art and then by analysing the case law on various directors’ duties, before discussing whether corporate art collecting is legitimate in light of those duties. The article examines the most important reasons why a corporation may collect art – as an investment, in furtherance of corporate social responsibility goals and in order to enhance the psychological well-being of employees – and concludes that while art collecting for such purposes does not amount to a breach of directors’ duties, this is subject to the requirement that a corporation put into place safeguards contained in a formalised art collecting and sponsorship policy, the key principles of which are stated at the end of the article.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".