Bias in the eye of beholder? 25 years of election monitoring in Europe
Bibliographic record
Abstract
Building on the original corpus of OSCE monitoring reports, the article analyses quarter of century of election monitoring in Europe and assesses the congruence of OSCE written assessments with expert views. We show that, overall, the OSCE monitoring reports are highly correlated and congruent with expert assessments. More importantly, the level of congruence between the two increases with time. However, we also identify various forms of biases rooted in strategic interests and institutional preconditions. Mainly, we show that OSCE has a strong and positive bias towards Russia and its allies when it comes to election assessments indicating defensive and lenient stances. We theorize this mechanism as a <i>pushback effect</i> and show that although Russia’s effort to cripple the activities of OSCE in the past two decades was not successful, OSCE was effectively forced into a defensive position producing less critical assessments than reality warrants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 teacher head, 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".