Bibliographic record
Abstract
Current studies on NATO burden sharing are only able to show some weak statistical trends between selective variables; they are unable to explain and show why this trend exists and why it occurred at particular times (or not). This is due to the dominant deductive and hypothesis testing research designs that prevent researchers to produce richer causal explanations or intersubjective understandings of how states, for example, construct and assign meaning to burdens or what forms of social representation, values, norms and ideals influence the making of (national) burden sharing decisions. Thus, we charge, the literature needs to adopt an eclecticist approach to studying NATO burden sharing – that is to combine rationalist with sociological approaches and methodologies highlighting the importance of intersubjective meanings and the role of social forces, norms, beliefs, and values. The article lays out what such a research programme might look like and how one could operationalise it.
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 | 0.023 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.019 | 0.054 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".