The dataset I explored was payroll for employees in all Los Angeles City Departments. The data ranged from January 1, 2011 to June 30, 2015. The data types included in the set were year, employment type, job class title, department title, hourly rate, and total earnings. In this dataset, a record is an individual working in the Los Angles City Department. The record contains the individual’s values for each data type.
Srinivasan and Wallack generally agree that an ontology is a method of classification of information. This categorization results in bias as the mere act of separating knowledge into groups creates a narrative. The narrative likely subverts the knowledge and manipulates it into a structure that is characteristic of the more powerful knowledge structure. Srinivasan states that an ontology is used to situate knowledge into a community. Regarding this dataset, the ontology is characterized by the data set listed above. It is contextualized around Los Angeles and is very Westernized as it uses terms, like dollars and mayor, that seem to be endemic to the United States. Ultimately, this ontology makes sense as it reflects the topic of the dataset.
This dataset will likely be most interesting to employees within the Los Angles City Department. Since the data is so limited and specific, it will cater to a smaller audience. Most individuals would not be interested in this data. It caters to an audience already interested or invested in the Los Angeles City Department.
The data illuminates the highest paid employees in the city department and the lowest paid. Employees of the police and fire departments are the highest paid, and veterinary aids and council aids are the lowest paid employees. This dataset elucidates that the Los Angeles society places the most importance on law enforcement. This fact may change according to different societal environments. For example, if the same data was collected in a country where law enforcement was not valued or it was not as highly developed, then the numbers would be vastly different.
However, the data does not include many factors that could widen its audience, such as job satisfaction and intensity. Under the same dataset, I would explore job satisfaction and perceived job intensity. I would either survey the individuals or access previous research. I would attempt to discern whether job satisfaction evolved over the years within the same jobs and if perceived job intensity correlated with pay and job satisfaction. Including these data types could garner more interest from a larger audience and be more universal. I believe more people are interested in job satisfaction and its relationship to job intensity. Therefore, this expansion of data would also widen the target audience. Furthermore, this data would be from the point of view of the individual rather than records from the department, therefore providing a new perspective.