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Record W2951147937 · doi:10.18438/eblip29550

Recent American Library School Graduate Disciplinary Backgrounds are Predominantly English and History

2019· article· en· W2951147937 on OpenAlexaffvenueabout
H. Robson MacDonald

Bibliographic record

VenueEvidence Based Library and Information Practice · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsCarleton University
Fundersnot available
KeywordsDisciplinePopulationLibrary scienceScholarshipAccreditationMedical educationSociologyMathematics educationPolitical scienceMedicinePsychologySocial scienceComputer scienceDemography

Abstract

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A Review of: Clarke, R. I., & Kim, Y.-I. (2018). The more things change, the more they stay the same: educational and disciplinary backgrounds of American librarians, 1950-2015. School of Information Studies: Faculty Scholarship, 178. https://surface.syr.edu/istpub/178 Abstract Objective – To determine the educational and disciplinary backgrounds of recent library school graduates and compare them to librarians of the past and to the general population. Design – Cross-sectional. Setting – 7 library schools in North America. Subjects – 3,191 students and their 4,380 associated degrees. Methods – Data was solicited from every ALA-accredited Master of Library Science (MLS) program in the United States of America, Canada, and Puerto Rico on students enrolled between 2012-2016 about their undergraduate and graduate degrees and areas of study. Data was coded and summarized quantitatively. Undergraduate degree data were recoded and compared to the undergraduate degree areas of study for the college-educated American population for 2012-2015 using the IPEDS Classification of Instructional Programs taxonomic scheme. Data were compared to previous studies investigating librarian disciplinary backgrounds. Main Results – 12% of schools provided data. Recent North American library school graduates have undergraduate and graduate degrees with disciplinary backgrounds in humanities (41%), social sciences (22%), professions (17%), Science, Technology, Engineering and Math (STEM) (11%), arts (6%), and miscellaneous/interdisciplinary (3%). Of the humanities, English (14.68%) and history (10.43%) predominate. Comparing undergraduate degrees with the college-educated American population using the Integrated Postsecondary Education Data System (IPEDS) classification schema, recent library school graduates have a higher percentage of degrees in social sciences and history (21.37% vs. 9.24%), English language and literature/letters (20.33% vs. 2.65%), computer and information science (6.54% vs. 2.96%), and foreign languages, literatures, and linguistics (6.25% vs. 1.1%). Compared to librarians in the past, there has been a decline in recent library school graduates with English language and literature/letters, education, biological and physical sciences, and library science undergraduate degrees. There has been an increase in visual and performing arts undergraduate degrees in recent library school graduates. Conclusion – English and history disciplinary backgrounds still predominate in recent library school graduates. This could pose problems for library school students unfamiliar with social science methodologies, both in school and later when doing evidence-based practice in the work place. The disciplinary backgrounds of recent library school graduates were very different from the college-educated American population. An increase in librarians with STEM backgrounds may help serve a need for STEM support and provide more diverse perspectives. More recent library school graduates have an arts disciplinary background than was seen in previous generations. The creativity and innovation skills that an arts background provides could be an important skill in librarianship.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.008
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0250.010

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.

Opus teacher head0.034
GPT teacher head0.289
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2019
Admission routes3
Has abstractyes

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