Bibliographic record
Abstract
This chapter describes the Cross-National Equivalent File project (CNEF) – a project that harmonizes data from ongoing longitudinal surveys in 10 countries. CNEF is a compendium of data from general population household-based panels in Australia, Canada, Germany, Italy, Japan, Russia, South Korea, Switzerland, the United Kingdom, and the United States. We introduce CNEF – its genesis, the research infrastructure it operates on, and the structure of the CNEF database. Next, we discuss major issues specific to the project's cross-national harmonization of panel data. Regarding substantive variables, CNEF staff harmonize data for which there exist concepts that have clear theoretical definitions, are measured in objective units, and are, in principle, independent of country-specific culture. Hence, CNEF harmonizes data to empirically measure two types of conceptual variables: (i) concepts that are defined by objective conditions in the physical world (e.g. age, biological sex, pregnancy, etc.), and (ii) abstract concepts that can be defined rather precisely and are measured in objective units, such as income (household and personal), occupation, and health, among others. While harmonization of cross-sectional and longitudinal data requires the same attention to comparability of data across countries, longitudinal data additionally require attention to comparability over time. In discussing CNEF's evolution, we reflect on (i) lessons learned, (ii) how harmonization of existing data has influenced CNEF panel members to adopt harmonized survey questions, and (iii) challenges pertaining both to harmonization per se and its documentation. When CNEF partners to harmonize already collected data, they naturally discuss and sometimes coordinate to harmonize questions or survey designs in advance of the survey going into the field. We conclude with a brief outline of recommendations for researchers interested in harmonizing existing panel survey data.
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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 | 0.171 | 0.396 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.024 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".