Results-Oriented Cultures: Insights for U.S. Agencies from Other Countries' Performance Management Initiatives
Bibliographic record
Abstract
A letter report issued by the General Accounting Office with an abstract that begins "Strategic human capital management challenges face public sector organizations both here and abroad. The United States is not alone in examining how government agencies can use their performance management systems as a tool to foster a more results-oriented organizational culture. The Organization for Economic Cooperation and Development (OECD) has reported that its member nations have increasingly moved towards performance-based pay and appraisal systems that reward employees, hold them accountable for the quality of their work, and connect their efforts to organizational results. Four OECD member countries--Australia, Canada, New Zealand, and the United Kingdom--have begun to use their performance management systems to achieve results. Although the performance management initiatives in these countries reflect their specific organizational structures, cultures, and priorities, their experiences developing and implementing results-oriented individual performance management initiatives may provide U.S. agencies with information and insights as they implement strategic human capital practices. These countries have begun to use their performance management systems to create a "line of sight" between individual and organizational goals, use competencies to provide a fuller assessment of individual performance, link pay to individual and overall organizational performance, and foster organizationwide commitment to results-oriented performance management. Another way agencies seek to foster commitment in more results-oriented performance management systems is to involve stakeholders and include employee perspectives when designating or reforming their performance management systems."
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 | 0.019 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.023 | 0.011 |
| Scholarly communication | 0.028 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".