A person-centered and situated approach to populism in representative surveys
Bibliographic record
Abstract
A standard operationalization of populist attitudes is a variable-centered approach, approach assuming that any person can meaningfully position itself along a simple 'good people vs. bad elites/others' spectrum. This assumption does not fully align with the inherently discursive nature of populism which means that populism is embedded in narratives people make up, use, reject or change to pursue their goals in a sociocultural context. The main goal of the paper is to argue for a contextually sensitive and person-centered measurement of populism in representative surveys. However, surveys only offer a snapshot of who, when, where and how endorse populistic narratives, and it cannot account for changes in developmental dynamics of human mind, and consequentially populism. To illustrate the proposal, we may use a case of USA 2020 elections when according to chatGPT, three most prominent populistic narratives were: antiestablishment sentiment, economic populism and nationalism. All of them are vaguely represented by survey items in Comparative Study of Electoral Systems Module 5. ChatGPT helped us choose two resembling items per narrative. On a sample of 7389 participants, we have done a Latent Profile Analysis in R. Comparing 2 to 10 classes models, 4 classes had the best statistical fit and were intangible. Each class can be understood as a symbolic community because people share positions toward narratives and thereby convey underlying meanings. For further explorations of classes and their predictors, view https://osf.io/hxfbs/.
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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 | 0.104 | 0.217 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".