MétaCan
Menu
Back to cohort
Record W4408445282 · doi:10.5194/egusphere-egu25-1809

Protecting Blue Horizons – A role play to make an MPA work

2025· preprint· en· W4408445282 on OpenAlexaboutno aff
Cornelia E. Nauen, Marcelo Lino Morales Yokobori

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)New horizonsPhysicsThermodynamicsAstronomy

Abstract

fetched live from OpenAlex

Human activities are the major cause for what has been recognized as the 6th Mass Species Extinction. It is thus important to spread knowledge and raise awareness about the issues because we depend on biodiversity in ways that are not always apparent or appreciated. In December 2022, delegates from 196 governments adopted the Kunming-Montreal Global Biodiversity Framework (GBF) under the UN Convention on Biological Diversity (CBD). The GBF supports the achievement of the Sustainable Development Goals and sets out an ambitious pathway towards living in harmony with nature by 2050. Meanwhile, the lengthy ratification process is no guarantee of full enforcement after entry into force. Typically, different interest groups may resist top-down measures affecting them. This is known as the implementation gap of international treaties and agreements. Here we describe a role play intent on matching a key element of the top-down GBF, namely the establishment of interconnected marine protected areas (MPAs), with bottom-up awareness raising and deliberation among diverse stakeholders. Eleven characters of stakeholders have been developed through wide-ranging interviews and literature research. For each stakeholder an information sheet explains the context and his or her role. Based on the interviews, a general introduction and guidance for a moderator is provided together with a tentative schedule. Emphasis is placed on allocating sufficient time for the debriefing after a round of deliberations aiming at consensus towards establishing an effective MPA. The assumption is that the debriefing produces most learning about why biodiversity protection is essential and how to sustain a respectful dialogue process with persons holding different positions from one’s own. A first round of tests with young adults has already generated useful feedback allowing some improvements of the initial set. We propose the role play for wider use as a low-entry support for bottom-up participation in GBF implementation.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.048
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0110.007
Scholarly communication0.0080.008
Open science0.0010.009
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0480.010

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 head0.026
GPT teacher head0.268
Teacher spread0.242 · 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 designQualitative
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
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicModular Robots and Swarm IntelligenceFrench-language works237,207