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Record W4312219070 · doi:10.1163/1871191x-bja10145

Analysing Knowledge Diplomacy and Differentiating It from Soft Power and Cultural, Science, Education and Public Diplomacies

2022· article· en· W4312219070 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Hague Journal of Diplomacy · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Science and Diplomacy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDiplomacyPublic diplomacySoft powerTerminologyInternational relationsPolitical scienceConfusionPower (physics)Frame (networking)EpistemologySociologyPublic relationsSocial sciencePsychologyPoliticsEngineeringLawLinguistics

Abstract

fetched live from OpenAlex

Summary The purpose of this article is to explore the contemporary role of international higher education, research and innovation ( IHERI ) in international relations ( IR ). Using an interdisciplinary and conceptual approach, it examines how diplomacy and higher education scholars understand and label the rationales, strategies and contributions of IHERI to IR . The findings indicate that multiple terms are used, resulting in terminology chaos and confusion. The concept of knowledge diplomacy is proposed, defined and analysed as a comprehensive term to frame the role of IHERI in IR . The similarities and differences between knowledge diplomacy and related terms such as cultural, public, science and education diplomacy and soft power are examined. Issues that require further investigation are identified, with special attention given to the differences between using a knowledge diplomacy approach versus a soft power approach to understand the role of IHERI in IR .

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0010.002
Open science0.0010.001
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 head0.028
GPT teacher head0.364
Teacher spread0.336 · 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