Bibliographic record
Abstract
This article examines the ways in which diplomacy is adapting in the information age, to the increased pressures and opportunities that changes in information and communication technologies and capabilities provide. The interaction of technological, economic, political and social changes, such as globalisation, the development and rapid expansion of information and communication technologies, the increasing ability of citizens and non-governmental organisations (NGOs) to access and use these technologies, and the rise of transnational and co-operative security issues, are affecting the ways in which governments conduct their diplomacy. These changes are giving rise to what might be termed a ‘new public diplomacy’. This can be characterised by a blurring of traditional distinctions between international and domestic information activities, between public and traditional diplomacy and between cultural diplomacy, marketing and news management. The article focuses on a comparison of Britain and Canada. It argues that, in Britain, the new public diplomacy features a repackaging of diplomacy to project a particular image to an overseas audience, which is largely treated as a passive recipient of diplomacy. However, in Canada the new public diplomacy is characterised by a more inclusive approach to diplomacy, enabling citizen groups and NGOs to play a greater role in international affairs .
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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.
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".