Research on Grand Challenges: Adopting an Abductive Experimentation Methodology
Bibliographic record
Abstract
There has been a growing interest among management scholars in conducting research on grand challenges. Despite recognizing that studying such highly complex and uncertain phenomena likely requires more unconventional approaches, there has been very little methodological guidance provided to interested scholars. Drawing upon our own grand challenge projects undertaken over the past decade, we put forward a methodological approach we term 'abductive experimentation'. Such an approach is an action-oriented process of inquiry that cycles between generating 'doubt' and generating 'belief'. More specifically, abductive experimentation iterates between induction, abduction, and deduction to both generate and reconcile 'surprising' findings and causal mechanisms. While we submit abductive experimentation as a methodological approach particularly well suited to the study of grand challenges, we believe that the process depicted also provides a general roadmap for scholars seeking to dismantle the artificial dualism between theory and practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".