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Record W2518042604 · doi:10.1108/bl-03-2016-0015

The value of the MLS or MLIS degree

2016· article· en· W2518042604 on OpenAlexaboutno aff
Melissa Fraser-Arnott

Bibliographic record

VenueThe Bottom Line Managing Library Finances · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Variety (cybernetics)NarrativeKnowledge managementPsychologyMedical educationPublic relationsLibrary sciencePolitical scienceComputer scienceMedicine

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to examine the competencies, skills and knowledge obtained through a master’s degree in library and information science (LIS) and to identify those competencies that are most valued by graduates who obtained employment in non-library roles. These observations will contribute to an understanding of the transferability of LIS education which will assist LIS professionals and educators to frame LIS competencies in a way that appeals to employers outside of library settings who may not have any knowledge of LIS education or practice. Design/methodology/approach The grounded theory methodology was used with data collection taking the form of semi-structured interviews. Interviews were between 30 and 90 min in length and included career narratives, as well as responses to particular questions about different aspects of professional identity. Participants included graduates of master’s level LIS programs employed in a variety of positions including information managers, policy analysts, human resources specialists, marketers, vendors, taxonomists, search engine designers and information consultants. The participants were employed in sectors including government, information technology, aerospace, oil and gas and retail/online sales in both Canada and the USA. Findings The participants in this study found that their LIS education was valuable to their success in a number of non-library roles. Although the specific career paths of participants were each unique and the different roles they occupied required different combinations of skills, a list of key transferable LIS competencies could be identified. These included a focus on client service, the ability to identify need, and the ability to search for information and navigate databases. In addition, several participants observed that their coursework and opportunities to participate in internships, co-operative positions or work placements prepared them for such workplace conditions as deadlines and fast-paced environments. Originality/value This study involved a population of LIS graduates whose experiences have not been extensively examined in the past. This article helps to fill a gap in the understanding of the professional experiences of LIS graduates who pursue roles outside of libraries. In addition, the semi-structured interview technique allowed for deeper understanding of participants’ perceptions of which of their competencies, skills and knowledge were valuable to employees. This information was gained through answers to specific skills-focused questions intended to identify which competencies developed during their LIS education assisted them most in their careers, as well as larger career narratives. This study will have implications for library practitioners and educators. It will provide insights into valued skills for those who are designing or implementing LIS education programs, as well as LIS graduates who are seeking to market their skills to employers across industry sectors.

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)0.004
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0050.003
Open science0.0010.006
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0280.005

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 head0.029
GPT teacher head0.267
Teacher spread0.238 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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".

Quick stats

Citations13
Published2016
Admission routes1
Has abstractyes

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