Bibliographic record
Abstract
Abstract The new operational environment generated by the mass media revolution and the advent of the global information society lays the ground for a generalized re-emergence of public diplomacy (PD). After having been dismantled during the 1990s, this branch of foreign policy is undergoing a redevelopment phase within the chancelleries of many states around the globe. The growing salience of public opinion and the exponential development of the new information and communication technologies predispose this diplomacy of persuasion to play an increasing role at the forefront of twenty-first century international relations.Inspite of the increased importance that public diplomacy is acquiring, the question of its real effectiveness nevertheless remains unanswered. For the moment, governments are still unable to determine to what extent their PD initiatives are able to influence foreign audiences or contribute to the achievement of their foreign policy goals. Without a valid evaluation tool, PD will remain condemned to play a secondary role within states' foreign policy systems. This article addresses the main aspects of this issue by analysing the many technical and methodological problems that are attached to PD evaluation, exploring research avenues that could remedy these gaps, and thus helping to resolve a problem that is still underestimated yet bound to become increasingly important in the 'hyper-media' age of international relations.
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 | 0.112 | 0.244 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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.
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".