Organizational knowledge retention: international literature review
Bibliographic record
Abstract
ABSTRACT The aim of this work was to select an international knowledge fragment about the organizational knowledge retention theme, and to identify the characteristics of these studies. Methodologically, this research is guided by a constructivist perspective, with a qualitative-quantitative approach and with an exploratory and descriptive objectives. The Knowledge Development Process - Construtivist (Proknow-C) was used to select a Bibliographic Portfolio in line with the research theme and for bibliometric analysis. Among the main results, the co-authorship network demonstrated little relationship between the 48 researchers of the theme. However, the citation network showed constant and historically evolutionary referencing between articles. Most of the researches aimed to identify or present aspects of organizational knowledge retention. The main related topics were: knowledge management, human resources management and organizational structure. The most used dimensions were: knowledge retention, knowledge loss, information systems, organizational memory, turnover, retirement and knowledge transfer. United States of America, Australia and Canada stood out as host countries of organizations where empirical researches took place and also as headquarters of research institutions. The results showed the dynamics and characteristics of researches on organizational knowledge retention, in the sample used, and provide guidance for the evolution of the theme.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.061 | 0.094 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".