Bibliographic record
Abstract
This dataset was produced within the framework of the Making Electoral Democracy Work Project. It contains pre-election and post-election survey data from the 2014 European election in Lower Saxony. Making Electoral Democracy Work (MEDW) is an international collaborative project that was conducted between 2009 and 2017 by a large team of political scientists, economists, and psychologists from Canada, Europe, and the United States. Its goal was to examine how the rules of the game (especially the electoral system) and the electoral context (especially the competitiveness and salience of the election) influence the dynamic and reciprocal relationship between voters and parties. To do so, the project gathered data for different types of elections held in Canada, France, Germany, Spain, and Switzerland between 2010 and 2016. In each country, two subnational units, typically regions, were studied. The analyses were complemented by laboratory and online experiments, as well as data on the parties’ campaigns. A selection of the project publications can be found at http://electoraldemocracy.com/publications. For more information on the project visit the project website (http://electoraldemocracy.com) or read André Blais, “Making Electoral Democracy Work,” Electoral Studies 29 (2010): 169–70.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.025 |
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".