Bibliographic record
Abstract
When employing economic sanctions, what are the best practices to induce desired outcomes for the sending state(s)? Broad economic sanctions have been shown to be ineffective. Recognizing that sanctioning as a diplomatic strategy is unlikely to be abandoned, scholars have focused on making the case for smart timing and targeting of sanctions. Their arguments stem from deciphering the internal drivers of decision making within targeted states. Unlike work that is reliant on solely internal mechanisms, this paper enhances the understanding of targeted states by examining cost-benefit strategies of (1) individual leaders and (2) nation states that are in pursuit of strategic goals. This paper argues that when sanctions create large costs (anticipated or inflicted) on the target, those sanctions have a higher likelihood of producing successful outcomes regardless whether the sanctions are “smart” This study utilizes TIES data on sanctioning and Polity scores on democracy. I use ordinal logit and ordinary least squares regression to estimate the models and find strong support for the hypothesis.[1][1] I am thankful to Brooke Justus for her assistance in copy editing. I am also grateful to Daniel Tirone and the anonymous reviewers for their constructive feedback.
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 | 0.010 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.040 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.023 | 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".