Bibliographic record
Abstract
Wicked problems differ from tame ones in important ways that define significant challenges in resolving them. Among these differences are their lack of a prescriptive definition, their absence of a clear stopping rule, their emphasis on better or worse outcomes rather than right or wrong solutions, their uniqueness, and their demand that resolutions not make the problem worse. University graduates will take on central roles and leadership responsibilities for addressing the world’s wicked problems such as those identified as the United Nations Sustainable Development Goals. Those roles and responsibilities require advanced critical, systems, design, and ethical thinking skills and not just the disciplinary tactics and tame problem-solving abilities that largely comprise a university educational experience. This paper challenges the ways in which universities fail to equip their graduates with sufficient understanding of wicked problems and the approaches that offer the best chance to address them. The increasingly-granular structure of the academic year, the curricular emphasis on disciplinary rather than inter- or multi-disciplinary learning experiences, the lack of collaborative opportunities with those of other theoretical and practical perspectives, and the lack of intentional learning for critical, design, systems, and ethical thinking are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.028 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.004 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".