Declassification of professional social workers: A personnel issue facing the human services
The author discusses the issue of declassification of professional social work positions within public service agencies. The manner in which declassification is occurring, the reasons for it, and some strategies to block the trend are presented. An underlying theme of the paper is that the core issue in declassificalion of professional social work positions is the profession's inability to define clearly its domain of expertise. From a personnel standpoint this relates to issues of job analysis and evaluation and the inability of professionals to establish the fact that their contribution to a social service agency is unique, and by implication superior, to that offered by individuals possessing different qualifications and/or experience.
The Effect of Supervising Nonprofessionals on the Role of the Community Worker
The purpose of this research was to examine potential differences between the roles of community social workers who supervise nonprofessional staff and those who do not supervise nonprofessionals. Although it was hypothesized that the social worker who supervises nonprofessionals would be freed for more professional roles and would spend less time on roles that do not require professional skill, such differences were not found. Both devote about 40% of their work to tasks that do not require professional skill. Some differences were found between the two groups in those role factors that require professional skill. The social workers who supervised nonprofessionals spent a greater proportion of their time in guidance and activation of neighborhood committees, whereas those who did not supervise nonprofessional staff devoted more time to complex and executive roles.
Automatic classification of TV news articles based on telop character recognition
The purpose of this study is to develop a multimedia database system for TV news video data. TV news video data consist of speech, characters and images. In this study, telop characters are recognized and given to the news articles as indices for classification. At first, telop frames which include telop characters are detected and then the telop characters are extracted and recognized. Through morphological analysis of the recognized telop characters, keywords are extracted which consist of more than two characters. Their keywords are used as indices to classify the TV news articles. We carried out experiments for 30 days of NHK 5 minutes news and obtained 95.4% telop character extraction rate, 81.4% character recognition rate and 83.8% article classification rate. We improved the article classification rate by 43.4% through character recognition improvement from 44.1% to 81.4%.