Readiness for Change in the State Civil Apparatus: A Systematic Literature Review
Bibliographic record
Abstract
Readiness for change in the State Civil Apparatus is a necessity in the midst of a world situation full of turmoil, uncertainty, complexity and ambiguity. This readiness also has a central role in achieving success in implementing development and bureaucratic reform. The current reality is that readiness for change in the State Civil Apparatus is still low. The State Civil Apparatus has not been fully able to adapt and implement the expected changes and is still stuck with old work patterns. The aim of this research is to analyze and explain the factors that influence readiness for change in the State Civil Apparatus. This research is a systematic literature review using PRISMA (Preferred Reporting Items for Systematic Literature Reviews and Meta-Analyses) guidelines. The sample from this research is secondary data obtained through searching journals in the Google Scholar and Semantic Scholar databases. The journals found were then selected using inclusion criteria and quality assessment, resulting in three research literatures. The research results show that there are three factors or components that can influence the readiness for change in the State Civil Apparatus, namely: perceived organizational support, work engagement and leader-member exchange
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 | 0.024 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".