Change Scores, Composites and Reliability Issues in Cross-National Development Research
Bibliographic record
Abstract
While a number of researchers of world development examine social change using composite measures as indicators, there is a relative paucity of research on the reliability of these change score composites over time. We construct two development composites based simply on factor analysis, one economic and one social, and then perform reliability analysis on these two development composites at two discrete points in time (i.e. 1970 and 1985) and their change over the 15-year period defined by their beginning and ending points. Despite evidence of reliable beginning and ending points, change in composites over time yield markedly different patterns of reliability. We conclude that if composite indicators of development are used in cross-national research to assess change, the reliabilities of their change should be addressed directly in addition to the reliabilities of their beginning and ending points. The risk of not doing so is faulty inferences with respect to theory.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".