Environment versus economy policy preferences: follow-up questions reveal substantial heterogeneity within the environmental coalition
Bibliographic record
Abstract
A growing body of research has attempted to measure the propensity of individuals to prioritize environmental issues over economic objectives. A frequently used item for this purpose asks respondents to choose between protecting the environment at the cost of less economic growth and growing the economy at the expense of less environmental protection (Cosgrove, 1982; Dunlap, 1991, 2008; Diekman & Franzen, 1999; Guber, 2001; Newport, 2009; Anderson & Stephenson, 2011). Despite its prominence in the literature, several critiques regarding the forced-choice dichotomous environment-economy trade-off question have been raised (Dietz, Stern, & Guagnano, 1998; Klineberg, McKeever, & Rothenbach., 1998; Dunlap & Jones, 2002; Hand & Macheski, 2003; Kaplowitz, Lupi, Yeboah, & Thorp, 2013). Building on this work, we demonstrate that the pool of environmentalists identified using forced-choice dichotomous questions constitutes a heterogeneous coalition of individuals composed of more and less assertive supporters of environmental protection. In fact, the pool of respondents who initially indicate support for environmental protection decreases substantively when respondents are explicitly asked a follow-up question probing the willingness to continue prioritizing the environment in the face of direct economic costs. Here, we adopt this measurement strategy, exploring its capacity to both distinguish between strong and soft environmentalists and to explain attitudes toward one of the most contested environmental policies: a carbon tax.
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".