A mentoria como prática da gestão do conhecimento e da educação corporativa: um desafio possível?
Bibliographic record
Abstract
Knowledge has become the new source of wealth and demands resources such as access to and use of information.In order to maintain competitiveness and ensure operational efficiency while continuing to innovate and incorporate new knowledge into their production processes, organizations are guided by a culture of learning, since knowledge creation is facilitated in organizational environments of continuous and permanent learning.Knowledge management and networked learning are configured as a way of enhancing knowledge assets and are implemented through corporate education or corporate universities.Knowledge and human capital management often take unrecognized forms.The objective of this study is to analyze whether the mentoring process of a given public institution can be considered a practice of knowledge and human capital management in the organization's corporate education system.Although interest in mentoring is relatively recent in Brazil, it has been occurring for decades in countries such as Canada and the United States.The approach of this investigation is qualitative, with the option of a case study design.The case studied was the organizational mentoring program of a federal public institution with a documentary data collection method.According to the literature gathered for this article, the program can be considered as formal, organizational, peer-to-peer, and virtual mentoring.Analyzing the content of the collected material, from the perspective of the knowledge conversion modes of the SECI model by Nonaka and Takeuchi (1997), it can be seen that all four modes of the knowledge spiral were contemplated, enabling the integration, sharing, and use of knowledge in order to improve personal and organizational skills through the mentoring strategy.As a proposal for future research, since there are still no significant studies on organizational mentoring, it is suggested that mentoring processes within for-profit organizations be investigated in relation to the application of the SECI model.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; both teacher heads agree on what is shown here.
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".