Promoting novelty, rigor, and style in energy social science: Towards codes of practice for appropriate methods and research design
Bibliographic record
Abstract
A series of weaknesses in creativity, research design, and quality of writing continue to handicap energy social science. Many studies ask uninteresting research questions, make only marginal contributions, and lack innovative methods or application to theory. Many studies also have no explicit research design, lack rigor, or suffer from mangled structure and poor quality of writing. To help remedy these shortcomings, this Review offers suggestions for how to construct research questions; thoughtfully engage with concepts; state objectives; and appropriately select research methods. Then, the Review offers suggestions for enhancing theoretical, methodological, and empirical novelty. In terms of rigor, codes of practice are presented across seven method categories: experiments, literature reviews, data collection, data analysis, quantitative energy modeling, qualitative analysis, and case studies. We also recommend that researchers beware of hierarchies of evidence utilized in some disciplines, and that researchers place more emphasis on balance and appropriateness in research design. In terms of style, we offer tips regarding macro and microstructure and analysis, as well as coherent writing. Our hope is that this Review will inspire more interesting, robust, multi-method, comparative, interdisciplinary and impactful research that will accelerate the contribution that energy social science can make to both theory and practice.
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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 | 0.804 | 0.833 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.024 | 0.016 |
| Science and technology studies | 0.012 | 0.079 |
| Scholarly communication | 0.041 | 0.033 |
| Open science | 0.010 | 0.033 |
| Research integrity | 0.014 | 0.024 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".