Methodological Challenges in Sustainability Science: A Call for Method Plurality, Procedural Rigor and Longitudinal Research
Bibliographic record
Abstract
Sustainability science encompasses a unique field that is defined through its purpose, the problem it addresses, and its solution-oriented agenda. However, this orientation creates significant methodological challenges. In this discussion paper, we conceptualize sustainability problems as wicked problems to tease out the key challenges that sustainability science is facing if scientists intend to deliver on its solution-oriented agenda. Building on the available literature, we discuss three aspects that demand increased attention for advancing sustainability science: 1) methods with higher diversity and complementarity are needed to increase the chance of deriving solutions to the unique aspects of wicked problems; for instance, mixed methods approaches are potentially better suited to allow for an approximation of solutions, since they cover wider arrays of knowledge; 2) methodologies capable of dealing with wicked problems demand strict procedural and ethical guidelines, in order to ensure their integration potential; for example, learning from solution implementation in different contexts requires increased comparability between research approaches while carefully addressing issues of legitimacy and credibility; and 3) approaches are needed that allow for longitudinal research, since wicked problems are continuous and solutions can only be diagnosed in retrospect; for example, complex dynamics of wicked problems play out across temporal patterns that are not necessarily aligned with the common timeframe of participatory sustainability research. Taken together, we call for plurality in methodologies, emphasizing procedural rigor and the necessity of continuous research to effectively addressing wicked problems as well as methodological challenges in sustainability science.
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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 | 0.805 | 0.740 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.011 | 0.074 |
| Scholarly communication | 0.031 | 0.041 |
| Open science | 0.012 | 0.031 |
| Research integrity | 0.014 | 0.031 |
| Insufficient payload (model declined to judge) | 0.004 | 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".