Bibliographic record
Abstract
From the Clinton-Gore attempts to “reinvent government” to public reform laws enacted worldwide, it is apparent that governments are responding to calls for improved public sector performance and accountability to citizens (Farazmand, 2004). Within these efforts, the introduction of electronic government (e-government), that is, the Web-based provision of information and services to government stakeholders (Lee, Tan, & Trimi, 2005), is among the most noteworthy. Under the e-government umbrella, e-human resources management (e-HRM), meaning the Web-based management of the employer-employee relationship (Ruël, Bondarouk, & Looise, 2004), is increasingly seen as conducive to enhanced government functioning. The goal of this article is to discuss governments’ use of e-HRM as a means of improving overall public sector performance. Technology and the strategic management of employees can contribute to firm performance and thus highlight the potential usefulness of e-HRM to the public sector. Following a brief overview of e-government, this article defines e-HRM, outlines its advantages, describes the experiences of select countries, and outlines a few challenges surrounding implementation. The article concludes with speculation on future trends.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".