Bibliographic record
Abstract
Abstract This article develops the following arguments: (1) globalization, that is, the sustained increase in trans-border flows of goods, services, capital, images, and data has changed many things in our interaction with the international environment. (2) These changes are partly related to technological changes in communications and transport, and are also due to the changing nature of the international system under the twin pressures of the compression of time and space created by globalization. (3) The disconnect between these increased international flows and the lack of suitable global governance institutions and mechanisms to deal with the challenges created by them has added numerous issues to the international agenda, with which often understaffed and underfunded foreign ministries find it increasingly hard to cope. (4) Paradoxically, at a time when these international challenges appear to be especially urgent, foreign ministry budgets are being cut, thus making it even more difficult to cope with these challenges. (5) One reason for this is the lack of institutional and behavioural adaptation by foreign ministries and diplomats themselves to this new environment. (6) The main features of ‘network diplomacy’ are elaborated on, as are the conditions under which they are especially pertinent.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.005 |
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".