A Review of Fiscal Measures to Benefit Heritage Conservation
Bibliographic record
Abstract
This research paper explores to use of fiscal incentives for heritage conservation in a range of countries in Western Europe (Belgium, Denmark, France, Germany, Ireland, Italy, Netherlands, Spain and the United Kingdom) and North America (Canada and USA), to see how they are used and how effective they are.\nThe main incentives are: Income tax deductions and credits for costs incurred in heritage conservation activity; Income tax credits for the provision of social housing in heritage buildings; Property tax exemption, abatement or freeze for heritage buildings; Value added (sales) tax concessions or rebates relating to heritage conservation activity; Use of tax systems to provide an incentive to donations and corporate sponsorship activity through the establishment of heritage trusts and foundations; Inheritance, gift and capital gains tax exemptions and concessions. \nA key message to emerge from this research is that fiscal measures have a key role to play in heritage conservation, by providing incentives to owner-occupiers, investors and developers without requiring actual expenditure by government. The multiplier effects of expenditure on heritage conservation means that the income foregone by government in providing these incentives can be recouped through increased tax revenue, through the positive impact that heritage conservation has on neighbourhood revitalisation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".