Business Giving, the Tsunami and Corporates as Rock Stars: Some Implications for Arts Funding?
Bibliographic record
Abstract
Financial and cultural aspects of corporate giving by UK and non-UK companies in response to the December 2004 South Asia Tsunami disaster are explored in this article. Literatures on corporate giving rationales, concepts of disaster and donor activity in disasters provide an underpinning. The article seeks to make connections between this high profile if short-lived business giving and the funding of the arts that is sought from business; and to draw tentative lessons for arts funding when seeking business support. The giving accounts in the wake of the Tsunami from a non-probability sample of 56 UK companies and 16 non-UK companies were examined. Reported online to the UK charity Business in the Community, these accounts were accessed in February 2005 and scrutinized thematically. Concurrently, company financial profiles to accompany giving figures were constructed. Although linkages between donation levels and financial performance were lacking, emerging themes included the role of employees, influencing company giving and creating a climate of expectation of firms' contributions. These developments may have important implications for business funding for the arts, where leading philanthropists are prominent as individuals in the giving landscapes; but employees' collective involvement is not marked. Alternatively, cultivation of employees as would-be donors, indirectly via their firms, may be a more secure, if lower level route to funding for some arts organizations than dependence on high profile business leaders. The article considers alternative scenarios for company giving in disaster contexts, including as a sustained and lasting giving theme or as company support as a ‘one-off’ event, rock-star style. The likely development of employee power as a key element in company giving is explored; and its wider meanings for funding in arts settings, (where the giver as rock star heroine/hero is also prominent) are considered.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".