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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 OpenAlex

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fundUn bailleur canadien est enregistré sur le travail.
venuePublié dans une revue dont le pays d'attache est le Canada.
no affAucune affiliation canadienne : ce travail est invisible pour une base fondée sur la seule affiliation.
Aucune affiliation canadienne. Une base fondée sur la seule affiliation (le devis habituel) n'aurait jamais vu ce travail. C'est l'un des travaux qui justifient l'inversion de la base.

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.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,008
score de la tête « metaresearch » (Gemma)0,046
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Communication savante, Charge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCommunication savante
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,530
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0080,046
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,004
Études des sciences et des technologies0,0000,000
Communication savante0,0020,412
Science ouverte0,0030,002
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

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