Making Job Postings More Equitable: Evidence Based Recommendations from an Analysis of Data Professionals Job Postings Between 2013-2018
Bibliographic record
Abstract
Abstract Objective - Over the last decade, many academic libraries have hired data professionals to offer research data services. As these positions often require different types of experience than traditional librarian positions, there is an increased interest in hiring professionals from outside the typical library and information science (LIS) pipeline. More broadly, there has also been an increased interest in academic libraries and higher education to incorporate the principles and practices of diversity, equity, inclusion, and accessibility (DEI&A) into their work. These phenomena allow an opportunity to examine the growing area of data professionals and library hiring practices through the lens of DEI&A. Data was collected from 180 data professional job positions, including education, experiences, and skills, to better understand the evolving and complex landscape of data professionals and to provide evidence based recommendations regarding how the profession can enact meaningful and lasting change in the areas of DEI&A. Methods - The qualifications and responsibilities listed in data professional job postings from 2013 to 2018 were examined. Prior to analyzing the job postings, a codebook of 43 variables was developed. The 177 data professional job postings (corresponding to 180 positions) were independently analyzed, noting the presence of each variable, including the locations and the degrees of complexity sought. After coding, discrepancies were mutually resolved. Overall, the coding process had 94% intercoder agreement, which indicates a high level of agreement. Results - Over one-third of postings (n = 63, 35%) did not use the word “librarian” in the job title. Eighty-eight percent (n = 159) required a Master’s in LIS degree, but 67% (n = 119) also accepted an equivalent degree. Over half of the positions (n = 108, 60%) were also looking for an additional degree, most frequently a graduate degree. The median salary of the positions listing a quantitative value was $57,000; however, this value may not be accurate because only 26% of job positions (n = 47) gave a quantitative salary. From the research data management skills mentioned, general data management (n = 155, 86%), data repositories (n = 122, 68%), and data curation (n = 101, 56%) appeared most frequently. Libraries were also looking for traditional LIS skills and experiences, including instruction (n = 138, 77%), consultation (n = 121, 67%), and a public services perspective (n = 69, 38%). Conclusion - The results show that academic libraries are trying to recruit candidates from outside the traditional academic library pipeline. Research data activities (a non-traditional area for LIS) and traditional LIS areas were both frequently mentioned. Overall, these job positions should be written through a more intentional lens of DEI&A. This would help to make data professional positions more diverse and inclusive, while also helping academic libraries to reach their goal of recruiting outside of LIS. A set of concrete DEI&A recommendations are provided that are applicable for writing all library positions, so that readers can put these results into action and enact meaningful change within the profession.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.316 | 0.583 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.009 | 0.017 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".