Bibliographic record
Abstract
Developing strategies for successfully transferring knowledge, skills and attitudes from a training programme to the workplace continues to be a key challenge facing organisations. Studies have found that, in general, employees transfer less than 10% of the training they acquire to their workplaces (Georgenson, 1982; Kelly, 1982; McGuire, 2014). Fitzpatrick (2001) and Saks (2002) argue that research regarding transfer of training could be complex because the figure of 10% has never been proven scientifically. Based on this study, we propose that the transfer of training models limit the transfer process because they focus solely on the whys and the why nots of the 10%, limiting the discussion to only the transfer of knowledge, skills, and attitudes from a training programme to a job. We contend that if the transfer of training research and discussion is broadened to include the remaining 90%, which is viewed as a lost job efficiency, one might discover some additional determinants contributing to the transfer of training. Therefore, this study is based on a new determinant called productive networking. In the study, interviews were used as a research instrument to investigate the significance of productive networking in the transfer of training process. Two bodies of literature were reviewed for the study. They were the frameworks of the transfer process set forth by Baldwin and Ford (1988) and Holton (2008), and the theories that support training transfer in organisations. The study determined that productive networking among trainees was a critical factor in the successful transfer of training.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".