A lingering grudge in the face of a power transition; the French-Canadian sovereignty movement in perspective
Bibliographic record
Abstract
I hypothesize that an individual that has experienced a power transition, as measured by income, will be more likely to challenge the status quo than one that has not. The hypothesis is tested by relying on a mathematical probability model and is then corroborated through a qualitative analysis. This analysis was accomplished by using a dataset that was collected in Québec during the time of the referendum; the final vote of each individual was contrasted with their relative income and dissatisfaction with the federation. While income alone will not bring about a secessionist vote, the increase in the likelihood of such a vote can be contrasted across income levels to show the effects hypothesized. The result being that when we control for dissatisfaction at a middle point, those voters with low income will be less likely to vote for secession than those with a high income at the same level of dissatisfaction. Again, I find that at middle levels of dissatisfaction "income" has a decisive role in determining an individual's vote in the referendum.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".