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Record W3168126397 · doi:10.31222/osf.io/gj4aq

The Globalization of Science: The Increasing Power of Individual Scientists

2021· article· en· W3168126397 on OpenAlexfundno aff
Marek Kwiek

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsnot available
FundersEuropean CommissionStrongMcGill UniversityUnited States Agency for International Development
KeywordsGlobalizationPolitical scienceCuriosityGlobal networkGlobal public goodWork (physics)Public goodPublic relationsEconomicsComputer scienceEngineeringNeoclassical economicsLawPsychologyTelecommunications

Abstract

fetched live from OpenAlex

National science systems have become embedded in global science and countries do everything they can to harness global knowledge to national economic needs. However, accessing and using the riches of global knowledge can occur only through scientists. Consequently, the research power of nations relies on the research power of individual scientists. Their capacity to collaborate internationally and to tap into the global networked science is key. The constantly evolving, bottom-up, autonomous, self-regulating, and self-focused nature of global science requires deeper understanding; and the best way to understand its dynamics is to understand what drives academic scientists in their work. We are particularly interested in the contrast between global science as a largely privately governed and normatively self-regulating institution and global science as a contributor to global collective public goods. The idea that science remains a state-driven rather than curiosity-driven is difficult to sustain. In empirical terms, we describe the globalization of science using selected publication, collaboration, and citation data from 2000-2020. The globalization of science implies two different processes in two different system types: the growth of science in the Western world is almost entirely attributable to internationally co-authored publications; its growth in the developing world, in contrast, is driven by both internationally co-authored and domestic publications. Global network science opens incredible opportunities to new arrivals—countries as well as institutions and research teams. The global system is embedded in the rules created by scientists themselves and maintained as a self-organizing system and nation-states have another major level to consider in their science policies: the global level. Globalization of science provides more agency, autonomy, collegiality, and self-regulation to scientists embedded in national science structures and involved in global networks.

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)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.012
Science and technology studies0.0040.012
Scholarly communication0.0150.020
Open science0.0010.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.400
GPT teacher head0.565
Teacher spread0.164 · 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.

Study designObservational
DomainEvaluation
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

Citations20
Published2021
Admission routes1
Has abstractyes

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