Bibliographic record
Abstract
Abstract: Traditionally, there have been a small number of awards for public servants, given for lifetime career achievement by the most senior public servants. In the last decade, there has been a proliferation of new types of awards, including group or individual achievement awards to public servants at all levels, awards to functional or occupational groups, innovation awards, gain‐sharing awards, and organizational performance or quality awards. The proliferation of new awards is seen as a response to morale‐reducing cutbacks within the public sector as well as to external criticism of the public sector. The article discusses the possible impacts, both beneficial and harmful, of recognition and awards programs. It sets out how a benefit‐cost analysis of any award could be undertaken, showing the significance of time required by applicants among the cost factors. The article concludes with recommendations about how better to manage a number of the elements of awards, including publicity, the composition of selection committees, the award itself, feedback given applicants, and a department's comprehensive portfolio of awards. Sommaire: Traditionnellement, un très petit nombre de prix ont été décernés aux plus anciens et plus hauts fonctionnaires pour leurs réalisations au cours de leur carrière. Ces dix demières années, il y a eu une prolifération de nouveaux types de prix, y compris des prix pour réalisations collectives ou individuelles décernés aux fonctionnaires de tous les niveaux, des prix pour les groupes fonctionnels ou professionnels, des prix ä d'innovation, des prix de partage des gains, ainsi que des prix du rende‐ment ou de la qualité. On perçoit cette multitude de nouveaux prix comme une réaction compensant l'effet nocif qu'ont les coupures budgétaires sur le moral de la Fonction publique ainsi que les critiques extemes envers le secteur public. Dans cet article, on examine les répercussions possibles des programmes décemant des prix et des honneurs, répercussions qui peuvent être h la fois bénéfiques et nocives. On précise la maniére dont on pourrait entreprendre une analyse avantage‐coût de tout prix de ce genre, en explicitant l'importance de fadeurs de coût et de temps pour les postulants. L'article conclut en proposant des recommandations pour une meilleure gestion de certains éléments de ces prix, dont la publicité, la composition des comités de sélection, le prix hi‐même, les avis furnish aux postulants et la liste détaillee des prix d'un ministére.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".