I examined the Payroll by Position data set, which organizes the payroll information for all Los Angeles City Departments since 2013. The data categorizes financial details based on very specific job positions, even separating roles with the same name into a different row (i.e. “Senior Management Analyst II” versus “Senior Management Analyst I,” and even those position with the exact same name is distinguished by record number/ employee id). The job department title and year constitutes a record in this dataset, branching into 34 categories including: record number, class title, employment type, projected annual salary, Q1-Q4 payments, payments over base pay, & over base pay, total payments, base pay, permanent bonus pay, longevity bonus pay, temporary bonus pay, lump sum pay, overtime pay, other pay & adjustments, other pay (payroll explorer), MOU title, FMS Department, job class, pay grade, average health cost, average dental cost, average basic life, average benefit cost, benefits plan, and job class link.
In reference in Wallack and Srinivasan’s definition, this dataset’s ontology provides information on based on meta ontologies that may be mismatched with community ontologies, therefore leading to information loss and resulting inaccuracy. The data is collected and organized by the Los Angeles City Controller/Control Panel and therefore considered a large-scale dataset, making it difficult to incorporate into local, contextualized knowledge more specific to certain parts of the city. This ontology would make the most sense to a city official or manager, as it is helps them consider how much revenue is allocated to employee salaries and how to balance payroll for the most effective cash flow.
The dataset claims to hold a firm understanding regarding the payroll of a working class citizen of Los Angeles, as it lists a variety of job class levels from custodian to manager, part- and full-time, and lower- to upper-middle class salaries. Although the dataset provides a structure to determine which departments and offices is in need, the payroll information is monitored and controlled by the city and leaves out the perspective and input of the community. If the data-collection were influenced by the community ontology, the data would more resourcefully reflect societal concerns based off of economic conditions of certain regions, relevant skills and experience of individual employees/applicants, consistency for job roles and corresponding duties, etc.
I liked how thorough and thoughtful you were with your blog post about this particular dataset! They really broke down how much money each person is getting and for very specific things. I guess this provides more transparency between the public and employees. Like you said, I think you can tell a lot from this compilation of data regarding socio-economic class, inequalities in pay and other social issues. I think it would be helpful to include the why or maybe brief descriptions on the differences in these roles that result in more or less pay.