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Record W4226023028 · doi:10.18174/564381

Buitenlands eigendom van agrarische grond in Nederland, 2016-2021

2022· report· nl· W4226023028 on OpenAlexaff
P.P. Kuiper, M.J. Voskuilen, H.J. Silvis

Bibliographic record

Venuenot available
Typereport
Languagenl
FieldEnvironmental Science
TopicEnvironmental Conservation and Management
Canadian institutionsImpact
Fundersnot available
KeywordsAgricultureAgricultural landChristian ministryAgricultural economicsGeographyPeriod (music)Agricultural scienceBusinessEconomicsEnvironmental sciencePolitical scienceArchaeologyLaw

Abstract

fetched live from OpenAlex

At the request of the Ministry of Agriculture, Nature and Food Quality, this study provides insight into the extent and development of foreign ownership of agricultural land in the Netherlands between 2016 and 2021. Given the available sources, the following indications are given. Foreign ownership of agricultural land in the Netherlands decreased by 2,100 ha (-8%) to 24,600 ha between 2016 and 2021. This is 1.4% of the total area of agricultural land in the Netherlands. In the period 2016-2020, a total of about 3,700 ha of agricultural land in the Netherlands was purchased by foreign owners, and almost 4,500 ha of agricultural land was sold by foreign owners. This is 2 to 3% of the total traded area of agricultural land in this period.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.432
Threshold uncertainty score0.859

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.002

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.

Opus teacher head0.023
GPT teacher head0.242
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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