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Enregistrement W3210057673 · doi:10.18438/eblip29674

Making Job Postings More Equitable: Evidence Based Recommendations from an Analysis of Data Professionals Job Postings Between 2013-2018

2020· article· en· W3210057673 sur OpenAlexfundvenueno aff
Joanna Thielen, Amy Neeser

Notice bibliographique

RevueEvidence Based Library and Information Practice · 2020
Typearticle
Langueen
DomaineDecision Sciences
ThématiqueData Quality and Management
Établissements canadiensnon disponible
Organismes subventionnairesUniversity of Illinois at Urbana-ChampaignFlorida Institute of TechnologyOregon State UniversityGeorgia State UniversityUniversity of Massachusetts AmherstEast Carolina UniversityDrexel UniversityDePaul UniversityJohns Hopkins UniversityPrinceton UniversityUniversity of WashingtonVirginia Polytechnic Institute and State UniversityUniversity of MinnesotaSan José State UniversityUniversity of Texas at ArlingtonHarvard UniversityGeorgia Southern UniversityGeorge Washington UniversityWestern Michigan UniversityDartmouth CollegeYork UniversityUniversity of MiamiNorthwestern UniversityFlorida State UniversityIndiana University BloomingtonSan Diego State UniversityRice UniversityOhio State UniversityWashington University in St. LouisCity University of New YorkNorth Carolina State UniversityReed CollegePurdue UniversityYale University
Mots-clésProfessional developmentJob analysisCoding (social sciences)PsychologyEquity (law)Public relationsMedical educationComputer scienceSociologyMedicinePolitical scienceJob satisfactionSocial psychology

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,316
score de la tête « metaresearch » (Gemma)0,583
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesMétarecherche
DomaineSignal candidat: Incitatifs · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,684
Score d'incertitude au seuil0,844

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,3160,583
Méta-épidémiologie (sens strict)0,0010,002
Méta-épidémiologie (sens large)0,0030,004
Bibliométrie0,0190,018
Études des sciences et des technologies0,0080,005
Communication savante0,0150,020
Science ouverte0,0090,017
Intégrité de la recherche0,0040,006
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,350
Tête enseignante GPT0,478
Écart entre enseignants0,127 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; l’étiquette directe de Gemma et le classifieur distillé Codex s’accordent sur ce qui est montré ici.

Devis d'étudeObservationnel
DomaineIncitatifs
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations9
Publié2020
Routes d'admission2
Résumé présentoui

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