Human Capital: Insights for U.S. Agencies from Other Countries' Succession Planning and Management Initiatives
Bibliographic record
Abstract
A letter report issued by the General Accounting Office with an abstract that begins "Leading public organizations here and abroad recognize that a more strategic approach to human capital management is essential for change initiatives that are intended to transform their cultures. To that end, organizations are looking for ways to identify and develop the leaders, managers, and workforce necessary to face the array of challenges that will confront government in the 21st century. GAO conducted this study to identify how agencies in four countries--Australia, Canada, New Zealand, and the United Kingdom--are adopting a more strategic approach to managing the succession of senior executives and other public sector employees with critical skills. These agencies' experiences may provide insights to executive branch agencies as they undertake their own succession planning and management initiatives. GAO identified the examples described in this report through discussions with officials from central human capital agencies, national audit offices, and agencies in Australia, Canada, New Zealand, and the United Kingdom, and a screening survey sent to senior human capital officials at selected agencies."
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".