What Do We Mean By Historical Legacy?
Bibliographic record
Abstract
In the nearly quarter century since the collapse of communism a great many outcomes, from patterns of democratic consolidation and electoral behavior to state-society relations and cultural attitudes, have been attributed to legacies of the past. Some of these outcomes, such as a mistrust of politics or the dominance of the state sector, are attributed to legacies from the communist past. Other outcomes, such as nationalist conflict or enduring support for rightist parties, are traced back to the interwar period and beyond. What unites this research and related efforts to account for outcomes in other parts of the world is an abiding sense that to fully understand the present it is necessary to take account of the past. Yet beyond this common goal there is little consensus on what we as researchers mean when we conclude that an outcome is a historical legacy. This essay offers a preliminary assessment of that meaning.
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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 | 0.023 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.013 | 0.078 |
| Scholarly communication | 0.025 | 0.054 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 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".