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Record W2470891001 · doi:10.1139/cjfr-2015-0298

Fragmented national public media debate on international forest issues: a case study of Germany

2016· article· en· W2470891001 on OpenAlexvenueno aff
Jacqueline Logmani, Max Krott, Lukas Gießen

Bibliographic record

VenueCanadian Journal of Forest Research · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPolitical Influence and Corporate Strategies
Canadian institutionsnot available
FundersBundesministerium für Umwelt, Naturschutz, Bau und ReaktorsicherheitBundesministerium für Ernährung und LandwirtschaftEva Mayr-Stihl StiftungDeutsche Forschungsgemeinschaft
KeywordsNewspaperPolitical scienceMedia coverageThe InternetState (computer science)Public relationsSociologyLawMedia studies

Abstract

fetched live from OpenAlex

Over the past two decades, a number of international forest-related policies have evolved at the global and regional levels. The elements of this International Forest Regime Complex, however, are not equally relevant to all countries. This study analyzes the main actors’ positions in the public media debate in Germany and identifies links to the interests of the actors. First, the study explores the international regime related forest issues. A qualitative content analysis of the public media debate in one high-quality newspaper and in internet sources of relevant state and private actors analyzes the arguments of these actors in the issues. The results show that the debate of international forestry issues is fragmented and conflicting in Germany and that the conflict between use and protection structures in the public media debate is not supported by the data. Drivers of conflicting arguments are mainly associations representing protection, as well as user interests. The ministries avoid confrontation in public. Alliances between public agencies and lobby groups are seldom. Due to the strategic use of the public media, the debate does not indicate very well the existing conflicts about the main issues of the international forest regime in Germany.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.138
GPT teacher head0.361
Teacher spread0.223 · 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 teacher head, 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

Citations12
Published2016
Admission routes1
Has abstractyes

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