Quel parti va Gagner les Elections? Avantages et Faiblesses d'une question numerique [1]
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Whlch Party Will Win? Advantages and Weaknesses of a Numerical Question. This article evaluates the numerical question used in the 1997 Canadian Election Study which measures electors' perceptions of parties' chances of winning the election. At first, this question appears inappropriate for reliable research. At least three important weaknesses are associated with the question. First, the formulation contains some ambiguities. Second, the literature provides many pieces of evidences regarding the limited capacities of people to deal with probabilities. Finally, responses to the 1997 Canadian Election Study are not consistent with researchers' expectations regarding the form of these answers. However, the question provides reliable answers. Two empirical tests demonstrate that respondents give sensible answers. First, their perceptions follow the evolution of polls, and these perceptions also affect their voting behaviour.
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.
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 | 0.020 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 0.002 |
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 it