Projections towards resolving ingenuity gaps for complex learning societies: Prerequisites for sustainable development?
Bibliographic record
Abstract
ldquoIt isn't that they can't find solutions, it is that they don't see the problemrdquo Gilbert K.Chesterton (1874-1936) Critical destabilized resource uses that endanger individual and societal life require gap reductions between what is and what ought to be. In a genuine ldquolearning organizationrdquo (Peter Senge) and society, the progressive narrowing of ldquoIngenuity Gapsrdquo (Thomas Homer-Dixon) is likely assured with prevention of a Tragedy of the Commons (Garrett Hardin). What retards and prevents learning for innovations? Paradoxically, one primary obstacle relates to the most successful and entrenched applied solutions that can permeate societies. Such cultural patterns have been historically documented as ldquoprogress trapsrdquo(Ronald Wright), with consequences of societal decline and collapse. A systemic analytical approach may provide the means to discern what disconnects interacting flows within The Essential Tension (Thomas Kuhn) between an innovative potential and an actual solution, more specifically between ldquodescriptive and prescriptive technologiesrdquo (Ursula Franklin). A ldquoBoolean Dynamicrdquo(Stuart Kauffman) as a conceptual navigational tool might serve to advance means for unlocking gridlocks for optimizing requirements, like human-centric and techno-centric balancing as paradoxical contraries to sustain progressive development for complex societies. ldquoWithout paradox no progressrdquo, Niels Bohr (1885-1962).
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.012 | 0.028 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".