Proposição e avaliação de um modelo de transmissão de conhecimento coerente com comportamentos observados.
Bibliographic record
Abstract
Technological advances allow simulations and computational experiment to be attractive alternatives to proceed with scientific studies of some fundamental principles of real systems.This work was developed searching for a methodology to find a coherent model of knowledge (competence) transfer, in the study area of behavioral psychology of non-human primates.The experiment intends to assist researchers from the Laboratory of Cognitive Ethology of the Psychology Institute from USP, involved with the analysis of coconut break learning processes, based on knowledge transfer of a monkey group living on an island at Tiete Ecology Park, in Sao Paulo.The goal is the development of a computational model, implemented on a simulation platform, to assess virtual experiments on knowledge transfer in monkeys, evaluating the influence of peripheral activities on another specific one (coconut break).Furthermore, this thesis discusses the coherence between real and simulated data.Although secondary activities are not directly related to the specific one, there are evidences that they play a contribution role, a subject of this thesis too.The developed model considers both, the mechanism used to evolve and evaluate the knowledge transfer matrix (whose coefficients reflect the importance of each monkey relationship in their various activities).The computational platform is feed with real data, used also as a reference for comparison with simulation results.The behavior diffusion is performed inside a social network considering primates proximities (meetings).It is worth mentioning that the simulation runs on top of mathematical substrate not able to take into account all mental properties neither with fidelity all nuances of the social behavior.Therefore, the behavior of the agents in the simulation stage is constrained by those characteristics embedded in the used computational models, in such a way that their mental models and consequent behavior are naturally simplifications of the reality.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".