Efficiency in Public Administration Focusing on Strategic Alignment
Bibliographic record
Abstract
The purpose of this article is to measure the efficiency of Public Administration in the process of strategic alignment of people management with strategic organizational guidelines. The strategic alignment of Human Resources consists on adapting the people management strategy to the organization's strategy. An efficient institutional strategic alignment with the people management area is an essential condition for the success of the business. The first point for people management to be aligned with the organizational strategy regards to the fact that the human resources management strategy must derive from the corporate strategic plan. Recent studies demonstrate that there is an important change in the strategic focus related to people management, with the transition from a strategy focused on control to a strategy linked to commitment standing out as the most significant change. This article was developed through a bibliographic study on the strategic management of people in public administration and, also, by conducting a field research that covered 20 (twenty) units of the administrative structure from the government of the state of Rondônia - Brazil. The method used was the case study supported by a mathematical model developed by the authors, aiming to evaluate the event qualitatively and quantitatively in a more profound manner. The results demonstrated that the actions of the Public Administration, regarding the strategic alignment of the Human Resources area with the organizational strategy, are at an inadequate level of efficiency.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".