Learning dynamics and social interaction among knowledge workers in the electronics industry: evidence from Canada and Mexico
Bibliographic record
Abstract
This thesis explores and integrates the social, economic and cognitive factors of knowledge transfer using a theory that is grounded in the nature and dynamics of interactions among knowledge workers. At the individual level, learning must be accessible and collaborative in order to facilitate the production and transfer of new knowledge. At the macro level, organizations are required to provide access to information and knowledge sharing, and to be open to collaboration across their own boundaries. An important theoretical contribution of this study is the recognition of the interrelationships among factors that expand learning capabilities and perceptions of professional and personal development in the context of work. The model developed in this thesis represents an attempt to test an integrated theory of learning dynamics and social capital. Results suggest that social interaction in the workplace plays a key role in enhancing learning by creating opportunities for a wider range of activities through which knowledge is shared and implicit implications for personal development are expected. A web-based survey of employees in selected firms in the electronics sector in Canada and Mexico is used to test the model of learning and social interaction. There are two reasons for selecting the electronics industry: continuous learning has been a significant feature of this industry in which rapid technological changes are demanding constant skill upgrades and knowledge transfer, and the role of information technology in learning dynamics, and more specifically in social interaction, is broadly recognized in technology-driven firms as a factor for success, but how access to information can affect personal quality of life is unclear.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".