‘If they only knew what I know’: Attitude change from education about ‘fracking’
Bibliographic record
Abstract
A simple explanation for why another’s perspectives on unconventional gas development via hydraulic fracturing differ from one’s own is that people are uninformed. Such an answer employs the deficit model of communication and understanding—shown for a quarter century to be inadequate for explaining public perceptions and behaviors. A more likely explanation, but far more challenging for an easy “fix”, is that values fundamentally shape views. In autumn 2014, I taught an undergraduate course entirely on unconventional gas development (UGD) via hydraulic fracturing (often called “fracking”). I evaluated the effects of intensive education on attitudes about UGD by presenting my students with the same survey on the first and penultimate days of class. Overall attitudes changed little, despite substantial increases in self-reported knowledge and changes in beliefs about impacts associated with UGD. This poses a challenge for energy policies and regulation built off the assumption that additional education can readily change attitudes. I consider ways of approaching policy that respond to education’s limited effects on attitudes about UGD.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".