Knowledge Transmission in Light of Recent Transformations in the Workplace
Bibliographic record
Abstract
In a context of changing demographics and transformations to the world of work, concerns about age management are gradually turning into concerns about knowledge management. The vast experiential knowledge and diverse skills developed by workers to cope with the numerous situations encountered in the course of their work and to protect themselves against risks to their health and safety constitute part of the intangible assets vital to the sustainability of worker expertise and even the survival of the organization. Management practices play an important role in helping safeguard experiential knowledge in organizations. However, the transformations that have been taking place in recent years in response to an unstable economic climate have driven organizations to introduce a number of changes in workplaces. Three case studies, conducted in Quebec, each focused on the study of a specific occupation (film technicians, food service helpers, and homecare nurses), and based on interviews and observations made in the field, will be presented in an effort to describe the impact of some of these changes, namely precarious employment, flexible management practices and work intensification, on knowledge sharing in real work situations. The results suggest that by undermining work teams and increasing the workload of experienced workers, these changes actually hinder the knowledge sharing process. In fact, in such a context, the work teams are continually being reconfigured, which can demotivate experienced workers who constantly have to initiate new recruits despite already having a work overload. Possible avenues for research are proposed with a view to helping organizations cope with these changes in a way that supports the experiential knowledge transfer and sharing process so vital to organizational performance and the preservation of worker health.
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 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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".