Bibliographic record
Abstract
The main analyses that are presented in Chapter 3 consist of a graphical representation of how the McFadden pseudo-R2 of a vote-choice model that only includes socio-demographic variables fluctuates over time. The line graphs in Figures 3.1 and 3.2 suggest that the evidence for the idea that the impact of socio-demographic variables on the vote have weakened is limited overall. Here, I more formally test whether the patterns of change that are presented in Chapter 3 amount to a significant decline in the effect of socio-demographic variables on individuals’ vote choices. First, in Table D.1, I present the results of a series of ordinary least-squares (OLS) regression estimations, where the dependent variable is the McFadden pseudo-R2 statistic of a socio-demographics-only model and the independent variable is the year of the election. As can be seen from the estimates in Table D.1, there are four countries for which the overall time trend is negative and significant (Australia, Denmark, the Netherlands, and Sweden). With the exception of the Netherlands, however, the substantive importance of the estimated time trend is very small. In addition, there are two countries where the time trend is positive and significant (Canada and the United States).
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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 | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.472 | 0.109 |
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".