Reconceptualizing “feeling represented”: A new approach to measure how feelings of political representation are constructed
Bibliographic record
Abstract
Abstract In political representation, the concept of feeling represented has garnered significant attention in recent years. However, the prevailing conceptualization of feeling represented is limited in its scope and has effectively conflated a perception of being represented with feeling represented. We argue that such a conflation undermines our understanding of political representation, especially in the face of the constructivist turn in representation. Constructivist scholars have theoretically elaborated that representation results from a process in which a linkage is created between represented and representative. We argue that feeling represented is an essential part of establishing this representative relationship. By drawing on the recent constructivist turn in representation and applying key insights from psychology, we develop and test a reconceptualization of feeling represented. Through an online survey, American prolific users ( $$N=605$$ N = 605 ) were asked a number of questions related to feeling represented after witnessing representative claims by unelected representatives. Through both exploratory and confirmatory factor analysis, we establish that feeling represented is a multidimensional concept consisting of instrumental, expressive, and aversive dimensions. Thus, feeling represented is a subjective state consisting of clearly emotional components, and it can be measured as a response to unelected representation. Acknowledging this goes beyond the current constructivist account of representation and sets the stage for future research.
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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 | 0.014 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".