The Mental Map: A Specific Approach to Today's Political Behavior?
Bibliographic record
Abstract
If qualitative and quantitative approaches have frequently been opposed, the combination of both has also been praised. Nevertheless, few attempts, especially in the field of political science, have endeavoured to combine these two methodological approaches. Yet, a research tool seems to have the potential to break new ground in political science from both a qualitative and a quantitative perspective: the mental map. Often used in psychology (Piaget, 1987), and sometimes in geography (André et al., 1989; Fournand, 2003), political scientists have rarely used it (Laponce, 2001), however. On the basis of two empirical researches, one on Quebec City, the other one on federalism in Canada and Belgium, the aim of this paper is to discuss the pros and cons of the use of mental maps in the field of political science. Indeed, our purpose is to show that the mental map offers a contextual approach to understand individual as well as collective political behaviour, if the researcher follows strict methodological rules both in the implementation and in the analysis.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.043 |
| Scholarly communication | 0.016 | 0.021 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".