Abstracts of Papers Presented at the 2018 Annual Meeting
Bibliographic record
Abstract
The Spanish Civil War (1936)(1937)(1938)(1939) was one of the most devastating conflicts of the twentieth century, yet little is known about its long-term legacy.In this project we show that the war had a significant long-lasting effect on social capital, using geo-located data on historical mass graves and disaggregated modern-day survey data on trust.Preliminary results neither show a positive nor a significant effect of overall conflict on generalized trust.However, there is a significant negative relationship between exhumed mass graves and this same trust variable, pointing towards the deleterious long-term effects of political violence against civilians.To deal with the potential endogeneity of conflict, we use military plans of attack in conjunction with the historical (1931) highway network.We further decompose trust, finding negative effects of conflict on trust on institutions associated with the Civil War, but no effects when looking at trust on democratic institutions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.551 | 0.300 |
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".