Work Description

Title: Supporting Career Progression in Publishing: Job Description Data Open Access Deposited

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Attribute Value
Methodology
  • Job descriptions in academic publishing were acquired through two main methods: anonymous individual submissions (e.g., PDF or Word document of a single text) through a Qualtrics survey, and bulk submissions (e.g., a .zip file of many texts) from organizations (e.g., Association of University Presses; Jack Farrell Associates). Both externally posted job advertisements and internal position descriptions were accepted. Contributions were actively solicited from publishing professionals via social media posts, press releases, email lists to professional societies, presence at professional conferences, and word of mouth between May 2022 and May 2023. Manual checking was conducted as needed to remove duplicate texts across individual and bulk submission sources. In addition, all files of a different format were converted to Microsoft Word documents. In total, the corpus contained 1089 unique job description texts. A wide range/variety of positions (n=666) across functional areas were represented in the corpus. The majority of the texts came from publicly available job postings. This form of text is created to advertise positions and is typically neither as detailed nor as objective as internal position descriptions, which were contributed either individually or as a group from a particular publisher.

  • Corpus texts were qualitatively coded in order to extract standardized, relevant, and generalizable information. This structured data was collected via coder entry using a Google Form. Guided by the questions outlined above, Task Force members created a coding instrument that addressed multiple content areas within a job description. Coding variables included 15 position attributes like Decision-Making Authority (DMA) and Functional Area (FA), and 118 work attributes like skill types, leadership

  • A clean and anonymized dataset representing the qualitative coding data was constructed for public use. Data was stripped of columns that contained potentially identifiable and/or classified information (e.g., organization name, compensation information, description text), and columns used as internal records during processing stages were removed. These are the data published on Deep Blue. Due to privacy and legal concerns, the job descriptions are not fully released. The main privacy concern related to internal position postings submitted by individuals through the Qualtrics survey, while the main legal concern was the potential use of copyrighted material in textual descriptions of jobs.
Description
  • The study that produced this corpus was conducted by a joint Task Force on Career Progression charged by the Association of University Presses and Society for Scholarly Publishing in January 2022, and strengthened by the addition of the Association of Learned Professional and Scholarly Publishers in 2023. The goal of the Career Progression Task Force’s job classification project was to develop a dataset of job descriptions that identify typical skills and responsibilities associated with various roles in publishing, analysing these descriptions to elucidate the skills and qualifications needed for the jobs and career advancement and disseminate the results in a way that is useful to employers and employees alike. The work of the Task Force was made possible by the doctoral internship program at the University of Michigan's Rackham Graduate School, which supported the involvement of Michelle Lam and Lauretta Cheng. The project also involved master’s degree students at George Washington University and the University of Michigan.

  • In total, the corpus contained 1089 unique job description texts. A wide range/variety of positions (n=666) across functional areas were represented in the corpus. The majority of the texts came from publicly available job postings. This form of text is created to advertise positions and is typically neither as detailed nor as objective as internal position descriptions, which were contributed either individually or as a group from a particular publisher.
Creator
Creator ORCID
  • 0000-0002-9453-6695; 0000-0003-1992-6594; 0000-0003-1094-5246; 0009-0000-8449-8931; 0009-0000-3294-467X; , 0000-0002-4882-7205
Depositor
  • watkinc@umich.edu
Contact information
Discipline
Keyword
Citations to related material
  • Cheng, L., Heaney, K., Lam, M., Lord, J., Warren, J., Watkinson, C. (2024) Supporting Career Progression in Publishing through Systematic Analysis of Job Descriptions: A Cross-Industry Initiative, Learned Publishing
Resource type
Last modified
  • 04/12/2024
Published
  • 04/12/2024
Language
DOI
  • https://doi.org/10.7302/8bwe-t091
License
To Cite this Work:
Watkinson, C., Cheng, L., Lam, M., Lord, J., Heaney, K., Warren, J. (2024). Supporting Career Progression in Publishing: Job Description Data [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/8bwe-t091

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Files (Count: 9; Size: 54.9 MB)

Thumbnailthumbnail-column Title Original Upload Last Modified File Size Access Actions
README.txt 2024-04-07 2024-04-07 4.46 KB Open Access
PJDD_CodingData_Clean_Anon_Long_...m.csv 2024-04-07 2024-04-07 49.2 MB Open Access
PJDD_CodingData_Clean_Anon_Wide_Cat.csv 2024-04-07 2024-04-07 2.88 MB Open Access
PJDD_CodingData_Clean_Anon_Wide_Num.csv 2024-04-07 2024-04-07 713 KB Open Access
PJDD_DataVariables_Long.csv 2024-04-07 2024-04-07 3.66 KB Open Access
PJDD_WorkAttributes_Key.csv 2024-04-07 2024-04-07 17 KB Open Access
PJDD_CodingDataEntryForm.pdf 2024-04-07 2024-04-07 130 KB Open Access
PJDD_CodeBook.pdf 2024-04-07 2024-04-07 149 KB Open Access
PJDD_TaskForce_Methods_Overview.pdf 2024-04-07 2024-04-07 1.86 MB Open Access

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]

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