Addressing the imagination gap through STEAMM+D and indigenous knowledge
Bibliographic record
Abstract
Alex Manu describes an “imagination gap,” that is, “the gap between current capability and future possibility” [Manu A (2006) The Imagination Challenge: Strategic Foresight and Innovation in the Global Economy ]. Merriam-Webster defines imagination as “the act of forming a mental image of something not present to the senses or never before wholly perceived in reality”; imagination combines “creative ability” and “resourcefulness” [Merriam-Webster (2018) Imagination. Merriam-Webster Dictionary Online . Available at https://www.merriam-webster.com/dictionary/imagination ]. This paper considers two interdisciplinary fields in which distinct approaches have sought a solution to the “imagination gap” and have resulted in new research questions, methods, outcomes, and even philosophies. These are science, technology, engineering, arts, math, medicine, and design (STEAMM+D) and Indigenous research that establishes questions and methods from an integrated interdisciplinary worldview and the individual’s responsibilities toward community and land. By intertwining these approaches, it is possible for science and society to apply creative problem solving in addressing complex challenges, thereby fostering sustainable innovation.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".