Analysis of national human resource development (NHRD) policies of 2016 in South Korea with implications
Bibliographic record
Abstract
Purpose The purpose of this study is to explore ways to improve the effectiveness and efficiency of national human resource development (NHRD) policies led by South Korean central government agencies, identifying what policy decisions have been made and how they were implemented. Design/methodology/approach The authors collected data from the 2016 NHRD policy budget plans of the ministries and used content analysis. The unit of analysis was the fourth level, a sub-task, in each ministry’s policy budget plan. All coded policy contents were analyzed in terms of the centralized NHRD model, as well as through the perspective of developmental task theory. Findings The study yielded the following policy and implementation problems. First, the current system of NHRD policies established and implemented by individual ministries risks hampering the validity and effectiveness of the policies. Second, the structure of NHRD policy execution may cause similarity and redundancy across policies, thereby hindering the efficiency of the policies. Third, it is problematic when NHRD policies focus on solving short-term problems to the exclusion of long-term ones. Originality/value This study provides public recommendations to improve the effectiveness and efficiency of NHRD policies created by South Korea’s Central Government. If such an analysis has been made internally by the government, it has not been made publicly available. It also offers practical insights that might help to improve state-led human resource development policies for other countries – especially developing countries – that are planning to introduce central government-led NHRD or to improve existing NHRD policies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".