Date: March 25, 2024

Dataset Title: Supporting Career Progression in Publishing: Job Description Data

Dataset Creators: Cheng, L.S.P., Heaney, K., Lam, M., Lord, J., Warren, J.W., Watkinson, C.

Dataset Contact: Charles Watkinson watkinc@umich.edu

Funding: This research was sponsored by the Association of University Presses, the Society for Scholarly Publishing, and the Association for Learned and Professional Scholarly Publishing. The participation of Lauretta Cheng and Michelle Lam was made possible by the Rackham Doctoral Intern Fellowship Program of the University of Michigan. 

Research Summary:

While jobs in scholarly publishing are much sought after, there is little consistency in how individuals enter the profession and then advance their careers. A lack of transparency and documentation can be a barrier to increasing diversity in an industry that wrestles with its privilege. We report on a project initiated by three publishing industry associations to aggregate, normalize, and analyze job postings and internal position descriptions. After gathering over 1,000 unique descriptions, a group of knowledgeable volunteers qualitatively coded them. Consistency was checked using scripts developed by researchers from University of Michigan, who also analyzed the corpus. Preliminary visualizations show what skills suit potential applicants for various publishing positions, and which skills are most important to build for advancement. The findings can inform the development of products to make publishing a more equitable industry, such as interactive tools to match individuals with types of publishing jobs, well-formed template positions, and training programs that address skills gaps. 

Description:

The data in this work come from the Publishing Job Descriptions Dataset (PJDD). This dataset includes cleaned and anonymized versions of qualitative coding data for 1,089 publishing job descriptions texts, including Position Attributes (information about the position being described) and Work Attributes (ratings of importance for various elements of the work, such as work skills, as described for the position). The data was anonymized to remove any potentially identifiable or confidential information. 

Three versions of the data are available, as each may be preferred for different purposes. All versions have the same set of 13 Position Attributes, while the 118 Work Attributes are provided in: (1) wide format with categorial importance values (Wide_Cat), (2) wide format with numerical importance values (Wide_Num), and (3) long format with both categorical and numerical importance values, plus additional labels to group Work Attributes by type (Long_CatNum). Data variables (including all Position Attributes) are described for the most complete long format data. The full set of Work Attributes with grouping labels are listed in a separate key. The coding instrument (CodingDataEntryForm) and the codebook used during the process of qualitative coding (CodeBook) are also included. A slide deck with notes (TaskForce_Methods_Overview), used to present the methodology to the sponsoring organizations, is also included.


File Storage: Working files stored on Dropbox and on LC's personal computer. Finalized files deposited in University of Michigan's Deep Blue Data.

File List:

PJDD_CodingData_Clean_Anon_Long_CatNum.csv (long format with both categorical and numerical importance values)
PJDD_CodingData_Clean_Anon_Wide_Cat.csv (wide format with categorial importance values)
PJDD_CodingData_Clean_Anon_Wide_Num.csv (wide format with numerical importance values)

PJDD_DataVariables_Long.csv (description of data variables for long format data)
PJDD_WorkAttributes_Key.csv (list of work attributes and grouping labels)

PJDD_CodingDataEntryForm.pdf (coding instrument)
PJDD_CodeBook.pdf (code book)

PJDD_TaskForce_Methods_Overview.pdf (slides with notes, describing method)



Related publication(s):
Cheng, L.S.P., Heaney, K., Lam, M., Lord, J., Warren, J. W., & Watkinson, C. (2024). Supporting Career Progression in Publishing through Systematic Analysis of Job Descriptions: A Cross-Industry Initiative. Learned Publishing	

Use and Access:
This data set is made available under a Creative Commons Attribution 4.0 International Public License (CC-BY-4.0).

To Cite Data:
Cheng, L.S.P., Heaney, K., Lam, M., Lord, J., Warren, J.W., & Watkinson, C. (2024). Supporting Career Progression in Publishing: Job Description Data Set. [Data set]. University of Michigan - Deep Blue. [https://doi.org/10.7302/8bwe-t091]