Bibliographic record
Abstract
In the study of international relations, as indeed in all of the social sciences, reflections about the future are becoming increasingly numerous. They indicate frequently moreover a desire for systemization through recourse to rigourous techniques and procedures: the Delphi technique, the construction of scenarios, Systems analysis, operations research, decision matrices, graph theory, game theory, etc. This leads us to conclude often that the forecasting approach in international relations is undergoing a major quantitative and qualitative evolution. We seek to show however in this analysis that, contrary to appearances, forecasting research in international relations is characterized above all today by great epistemological weakness and by a remarkable incoherence, and that it is not therefore, for the most part, equal to its pretensions. We will attempt to determine why this is the case and if this situation is likely to change. In doing so, we will seek to identify both the possibilities and the limits of forecasting in this field.
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 | 0.035 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.034 | 0.029 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.011 | 0.038 |
| Insufficient payload (model declined to judge) | 0.007 | 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".