Bibliographic record
Abstract
Deze evaluatie is gewijd aan zes fiscale vrijstellingen voor bos en natuur.Deze vrijstellingen in respectievelijk inkomstenbelasting, vennootschapsbelasting en overdrachtsbelasting hebben een ondersteunende functie in het Nederlandse bos-en natuurbeleid.Het zijn kleine faciliteiten in budgettair opzicht.Gegevens over gebruik en effecten van de vrijstellingen zijn slechts beperkt vastgelegd.Door interviews is inzicht verkregen in hoe verschillende groepen betrokkenen de werking en effecten van de vrijstellingen beoordelen.Geconcludeerd wordt dat de vrijstellingen bijdragen aan het bereiken van doelen van het overheidsbeleid en geen hoge administratieve lasten en uitvoeringskosten met zich meebrengen.This evaluation is devoted to six tax exemptions for forested areas and other natural areas.These exemptions in income tax, corporation tax, and transfer tax respectively have a supporting role in Dutch forest and nature conservation policy.These are small-scale facilities from a budgetary perspective.Data on the use and effects of the exemptions have only been recorded to a limited extent.Through interviews, insight has been gained into how different groups of stakeholders assess the workings and effects of the exemptions.The conclusion can be drawn that the exemptions contribute to the achievement of the goals of the government's policy, and are not accompanied by excessive administrative burdens or high implementation costs.
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 | 0.036 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".