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Record W6949504520 · doi:10.5281/zenodo.15655132

Mapping of digital dialogues and identified EU-LAC agreements. Final version

2025· article· en· W6949504520 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Languageen
FieldComputer Science
TopicE-Learning and Knowledge Management
Canadian institutionsnot available
FundersEuropean Commission
KeywordsEuropean unionFocus (optics)Set (abstract data type)Order (exchange)Diversity (politics)AllianceListing (finance)sort

Abstract

fetched live from OpenAlex

EU pending approval. This final version of the mapping of digital dialogues and EU-LAC agreements tries to capture and showcase the work undertaken by the SPIDER consortium in order to provide a screenshot of the digital transformation landscape between the European Union and Latin America and the Caribbean. The analysis reflects the complexity and diversity of ongoing dialogues and commitments, which take place across multiple levels: at the bi-regional EU- LAC level; bilaterally between the EU and individual LAC countries or between specific LAC countries and EU Member States — such as Brazil and Germany; and within multilateral frameworks such as MERCOSUR, the Pacific Alliance or the United Nations even. This final version tries to showcase the most important take-aways. The focus is set first on listing and giving a short description of the dialogues and agreements and their resulting commitments in illustrative tables, to introduce the most important reoccurring dialogue formats and to sort them by frequency. Following, the dialogues and agreements are analysed with the focus on (1) main actors involved in the dialogues, (2) different themes and key areasaddressed, such as AI, 5G, blockchain, virtual research environments etc., (3) practical applications emerging form the dialogues and agreements, such as accelerators, hubs or software platforms deriving from the dialogues as well as (4) resulting commitments such as next steps, following dialogues, upcoming funding programmes, working groups etc. Eleven Dialogues and agreements are shown in an exemplary way. A special add-on is a specific agent-based Artificial Intelligence (AI) algorithm resulting from the analysis in this report, which is capable of processing the vast underlying database of this report within seconds and generate reports. It is available on the SPIDER website. The mapping has been compiled using a methodology-mix of (1) desk research and manual analysis; (2) AI-driven mapping and analysis; (3) partner connections, networking and consultation. Throughout the report, AI-created pictures have been used for illustrative purposes as well as small highlighting boxes for dedicated milestones in the digital landscape. The following results and achievements have been reached with this mapping as of status 31. March 2025: 84 dialogues that took place between 2022 and March 2025 as well as 12 upcoming dialogues that will take place between March 2025 and December 20261, resulting in a total of 96 dialogues of relevance for the EU-LAC partnership. While the SPIDER project initially envisioned identifying eight to ten key dialogues (Description of Action, DoA), this target was quickly surpassed during the initial desk research conducted by the SPIDER consortium already for the interim version of this report2 delivered in May 2024. The 96 dialogues are sorted by category, differentiating between: Bi-regional EU-LAC dialogues (26 dialogues + 4 upcoming) Bilateral dialogues between the EU and individual LAC countries (5 dialogues) or between specific LAC countries and EU Member States (see chapter 6) Multilateral dialogues (53 dialogues + 8 upcoming) In total, 62 agreements and it outcomes and commitments have been mapped across the various levels of engagement: EU-LAC level agreements (18 agreements) Multilateral agreements (41 agreements) Bilateral agreements between the EU and individual LAC countries (3 agreements) or between specific LAC countries and EU Member States (see chapter 6) A cross-cutting analysis on the following components of main actors, main themes, practical applications and resulting commitments has been undertaken: The main actors from the dialogues and agreements could be clustered into four categories: Political Actors, Research and Innovation Performing Actors, Economic Actors and Other Actors (e.g. Society at large). The main themes of the SPIDER project as mentioned in the description of action include AI, 5G, Blockchain, Cloud Computing, Cybersecurity and VREs. Through the initial workshop this was already expanded by further keywords such as digital transformation or quantum technologies. Overall, five core themes were identified: Digital Transformation & Strategy Technology & Innovation Data & Governance Inclusion & Social Development Regional & International Cooperation In total 54 sub-themes of relevance for the SPIDER project were identified. An in-depth analysis of the SPIDER’s core themes AI, 5G, Blockchain, Cloud Computing, VREs, Cybersecurity, Quantum Technologies and Gender is given in relation with specific dialogues and agreements. Eight Practical applications are introduced and showcased from the overall mapping list, in addition to the practical applications that could be identified for the core themes in the section 7.2.1. The resulting commitments could be sorted into seven categories: Action Plans, Agreements, Declarations, Memoranda of Understanding, Resolutions, Regulations, Roadmaps and Other (e.g. communication from the European Commission, joint programmes, statements). The report features an exemplary in depth-analysis of agreements resulting from the dialogues (11 in total), providing the basis for a pilot AI-based-analysis. A dedicated chapter on gender and underrepresentation of women in the use of digital transformation technologies has been added as well as a dedicated chapter on EU digital dialogues with other countries and regions in the world (outside of LAC), including with the Indo-Pacific Region, Sub-Saharan Africa, the USA and Canada. The Annex of D1.3 provides the full list of dialogues and agreements with their commitments with the following set of components: Name, Format, Place, Date, Characteristics, Actors, Themes, Practical Applications, Resulting commitments, Links.

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)0.005
metaresearch head score (Gemma)0.017
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 score0.103
Threshold uncertainty score0.344

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.009
Science and technology studies0.0020.001
Scholarly communication0.0070.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1030.023

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 head0.043
GPT teacher head0.242
Teacher spread0.199 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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