The Evolution of Databases in the Age of Targeted Sanctions
Bibliographic record
Abstract
Abstract Databases constitute key research tools in sanctions scholarship. Over the past few years, we have witnessed a proliferation of sanctions databases: while only a single dataset was available until 2009, this number had increased to five by 2020; thus, the choice has more than doubled in less than a decade. This essay assesses the evolution observed. It reviews the five major datasets, comparing some of their basic choices, and evaluates them along two dimensions: the extent to which they capture targeted sanctions and the degree to which they brought innovations to the subfield. We find that targeted sanctions are not adequately reflected in databases, which remain state-centric in their approach. We conclude that the crafting of new databases does not entail an incremental refinement in which each iteration renders its predecessors obsolete. Rather, the evolution observed has resulted in a diverse set of options with different emphases. We nevertheless observe that a trend toward innovation has yielded to one toward consolidation, more focused on enlarging the empirical testing ground than in innovating. We conclude by discussing implications for the development of sanctions scholarship.
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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 | 0.033 | 0.123 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.033 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.017 | 0.022 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".