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Record W6987973932

Vybrané otázky práva ochrany klimatu se zaměřením na proces přenosu technologií

2017· dissertation· en· W6987973932 on OpenAlexaboutno aff

Bibliographic record

VenueDigital Repository (National Repository of Grey Literature) · 2017
Typedissertation
Languageen
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsnot available
Fundersnot available
KeywordsTreatyKyoto ProtocolClimate changeAutonomyConventionInternational lawStrengths and weaknessesMontreal ProtocolConference of the parties
DOInot available

Abstract

fetched live from OpenAlex

The first chapter of this thesis discusses the recent development of climate change law. It explores the reason as to why it is at the centre of a global debate, which is predominantly due the increasingly pronounced consequences of climatic changes on human society and the environment. Furthermore, it describes the most important requirements in tackling the issues presented by international climate change treaties. This includes the United Nations Framework Convention on Climate Change, acting as a base for the whole international climate change regime, the Kyoto Protocol as a legal tool with specified emission targets and most recently, the Paris Agreement, which serves as an independent international treaty however is still under the guidance of the framework convention. The author predicts that the Paris Agreement will determine the future direction of this legal field and therefore puts particular focus on this treaty in the first chapter of the thesis. The paper aims to uncover its weaknesses - questioning the enforceability of some of the measures that rely on the autonomy of states to implement and the lack of ambition in some of its targets. The second chapter expands on one of the key issues related to the main topic. The author emphasizes how the importance of environmentally friendly...

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.768
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.000
Research integrity0.0010.001
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 head0.011
GPT teacher head0.296
Teacher spread0.285 · 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.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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