Towards complete knowledge for complex problems resolution
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
Human beings are complex. They learn through means of very different natures — thought, feeling, sensation, intuition — that complement each other without really understanding one another. Truly ideal knowledge would nevertheless involve all these means developed to their full potential and harmonized among them, which is almost impossible since, generally, one or two of them overwhelm the others. However, all would be necessary to understand and solve the crucial and equally complex problems — such as the ones related to immigration and climate change — that only a fully integrated multidisciplinary approach would allow dealing with adequately. It is in this perspective that we explore various categories of knowledge (meaningful, encyclopedic, etc.), as well as how and to what extent we can promote the development of what we have called “complete knowledge”, i.e., the richest and most complex that is accessible to an individual or a community. This would imply in practice to engage the learner with all the learning means available to him — they are associated respectively with speculation, appreciation, sensory experience and revelation. Despite the difficulty, an opening to other points of view could then take place, from the simple but already troubling tolerance of these points of view to their gradual integration in the learner’s mind. We argue that if a traditional, mostly linear, deductive approach is appropriate for the development of meaningful knowledge — provided certain characteristics of the learner, related to relevance and epistemology, are taken into account —, a dialectical approach should suit better the gradual development of the comprehensive knowledge, then increasingly best regarded as a symbol, required to foster collaborative work when multiple disciplines are involved. N.B. Part of this article reconsiders and deepens some of the ideas presented in Gagnon and Santos Ferreira (2018, in Portuguese). The masculine gender is used solely for the sake of readability.
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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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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