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Record W4387586174 · doi:10.18174/636845

Provinciaal beleid voor het versterken van de relatie tussen natuur en economie : een inventarisatie van provinciale invullingen van de ambitie ‘natuur en economie’ uit het Natuurpact

2023· report· nl· W4387586174 on OpenAlexaff
Mies van Aar, Allard Jellema, F. Langers, Didi van Doren, Esther de Wit-de Vries, Arlette van den Berg

Bibliographic record

Venuenot available
Typereport
Languagenl
FieldEnvironmental Science
TopicEnvironmental Conservation and Management
Canadian institutionsImpact
Fundersnot available
KeywordsRevenueWelfare economicsPolitical scienceEconomyEconomicsFinance

Abstract

fetched live from OpenAlex

One of the ambitions of the Nature Pact is to strengthen the relationship between nature and economy. We worked with the relevant stakeholders to inventory the policy strategies of the provincial governments in this area using the policy arrangement approach, and described the expectations, experiences and options for improving these strategies. Policy documents were systematically analysed, interviews were held with representatives from all the provincial governments and six concrete provincial strategies were selected for case studies. Four broad strategies for linking nature and economy were identified: nature-inclusive built environment, green revenue models, impact reduction and circular economy. Provincial policies for nature and economy are still fluid and under development; goals and policy strategies are not always clear and policy is often still experimental. Much experience has been gained over the past ten years and valuable networks have been built up, but little progress has been made on the ground. Before policies can be scaled up, ambitions, objectives, and guidelines and rules must be clearly defined.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.289
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0030.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0310.016

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 head0.034
GPT teacher head0.268
Teacher spread0.234 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2023
Admission routes1
Has abstractyes

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