Bibliographic record
Abstract
This study systematically assesses the G20 summit’s performance on digitalization across the key dimensions and suggests what has caused its particular pattern of performance thus far [Kirton, 2013]. It argues that the G20 summit’s digitalization governance has been increasingly successful. Its digitalization agenda steadily expanded since the beginning, with a major surge in 2016–17. G20 summits first addressed digitalization in response to the American-turned-global financial crisis of 2008. Then, G20 leaders acknowledged e-commerce as an important tool to manage the crisis. They then gradually expanded their agenda to finally focus on inequality, a root cause of antiglobalization. They thus moved from a crisis-response to a crisis-prevention approach. This spread and spike is seen in the G20’s direction-setting, decision-making and institutional development of global governance, but not in its delivery of its decisions. This overall performance was driven partly by the shocking surge in populism bred by inequality in the UK and U.S. in 2015 and 2016, by the failure of the established multilateral organizations in response, by the global predominance and equalizing capabilities of G20 members in specialized digital capabilities and their convergence on the economic growth through openness that digitalization brought. Yet this performance flowed primarily from the hosting of economically reforming China in 2016 and export-oriented Germany in 2017, whose politically secure leaders sought to shape digitalization for the benefit of all in response to the rise of populism and protectionism in the UK and the United States.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".