Multi-Paradigmatic Theorizing: Mixing Design and Exploration
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Design science research is becoming a major area in the IS discipline. Despite the growing popularity of DSR in IS, there is a lack of established guidance on how to conduct this type of research. Moreover, although DSR is considered a pluralistic area of research, few studies have proposed multi-paradigmatic methods for DSR. The current study suggests a new framework for theory development in DSR. The proposed framework integrates the previous DSR methodologies and differentiates between four components: design, design theorizing, explanatory theorizing, and data collection. A pluralist approach that integrates existing DSR components by coupling design and exploration, generating new knowledge (design theories) that can inform future representations is leveraged. This study steps outside the conventional theory development in DSR through employing a pluralistic perspective. We illustrate the framework with empirical research in the context of open strategic planning.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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 it