A study of in-service training as a function of management in federal government
Bibliographic record
Abstract
The increasing importance that is being placed upon the role of personnel administration and management in the Federal Government affords many research opportunities for the student of Public Administration. Today, there is a challenging opportunity for research in the process of training and development of personnel, and particularly in the field of in-service training. During the past quarter of a century there have been many studies and literature on the subject of training, but because of the dynamic setting in which personnel administration and management function, there is a need for continuous study. Irrespective of the importance that is now being given to personnel management in the Federal Government, there appears to be a lack of emphasis placed upon in-service training and development of personnel as a responsibility of management. It is because of this increasing importance of personnel management, the apparent need for greater emphasis on in-service training as a function of management, and the personal interest of the author in the subject, that this study has been undertaken. The study has involved a survey of outstanding literature in the field of training, governmental and industrial; personal observations of training concepts and practices, governmental and industrial; and personal experience as an in-service training staff member in departments of the Federal Government.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".