UNCOVERING THE INVISIBLE FLOWS OF SOCIALIZATION IN KM IN BRAZIL: Eduroam as one of the mapping sources
Bibliographic record
Abstract
The understanding of the importance of knowledge for the economy is already a consolidated reality.However, the search for understanding how to create an environment that allows and encourages the growth and use of knowledge in organizations is an evolving process.In teaching and research institutions, the search for knowledge and its recognition as the greatest product and contribution to society is undeniable.Given this scenario, this research seeks to investigate where and with what intensity the socialization process described in the SECI model has occurred within Brazilian institutions.For this purpose, records of use of the eduroam service over six months were used, processing 1,616,178 records referring to the period from 01/01/2016 to 06/30/2016.Social Network Analysis was used to build representations of these complex networks, with which it was possible to present the flow and relationships generated by social interactions between members of Brazilian and foreign teaching and research institutions within Brazil, highlighting among them USP, IFSC, UFRGS, UFSC and UNICAMP.It was found that only 5.1% of institutions participating in the flows are Brazilian, with the rest of the network made up of foreign institutions with a more intense participation of institutions coming mainly from Portugal, United Kingdom, Germany, Spain, United States, France and Canada.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".