Towards complete knowledge for complex problems resolution
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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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 | 0.025 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.025 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".