Supplementary material for "The Partisan Consequences of Secularisation: An Analysis of (Non-)religion and Party Preferences over Time" by Christopher D. Raymond, published in Secular Studies 3.1 (2021)
Bibliographic record
Abstract
These are the supplementary materials for an article published in Secular Studies entitled 'The Partisan Consequences of Secularisation: An Analysis of (Non-)religion and Party Preferences over Time', by Christopher D. Raymond, with DOI: 10.1163/25892525-bja10018. While we would expect secularisation to have important consequences for voting behaviour, data limitations in previous studies leave the specific implications of secularisation for Canadian electoral politics unclear. Using a data set covering the period between 1975 and 2005, this study examines which aspects of secularisation have affected the partisan balance of the electorate by estimating the effects of religious belonging, behaving, and believing on party preferences. The results show that while the effects of religion (and other social identities) have not changed over time, changes in the composition of the electorate resulting from the growing share of non-religious Canadians holding liberal views on questions of personal morality has benefited the NDP and undercut support for the Conservatives.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| 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) | 0.221 | 0.095 |
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".