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Record W2086579897 · doi:10.5703/1288284314841

Teaching Electronic Resource Management

2012· article· en· W2086579897 on OpenAlexaff
Sherri Ross

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicLibrary Collection Development and Digital Resources
Canadian institutionsPurdue Pharma (Canada)
Fundersnot available
KeywordsComputer scienceResource management (computing)Distributed computing

Abstract

fetched live from OpenAlex

Electronic Resource Management (ERM) is a specialization that impacts and is impacted by the work of librarians in public services, technical services and systems. All Library and Information Science (LIS) students that wish to work in libraries should have exposure to the concepts and practices of ERM. While LIS programs often convey this knowledge in bits and pieces through existing courses, I believe it is essential to pull this knowledge together into a single course, integrating the various components and providing an overarching perspective on the ERM environment. This presentation conveyed my experience developing an ERM course for the fall semester of 2009. I discussed the combinations of theory, concepts and practice that framed the course and I shared challenges that I encountered. ERM IN PRACTICE AND LIS EDUCATION In 2003, Fisher published an article that posited “the position title of Electronic Resources Librarian has been pre-empted by the public service sector of the profession” (p. 3). It was thought by many at the time that the functions of managing electronic resources would be integrated into the work of existing personnel. As challenges providing technical access to electronic resources arose, systems personnel became involved. As electronic resources proliferated and interfaces evolved, evaluation and selection responsibilities were given to reference and collection development staff. And as licensing terms and pricing schemes became more complex, acquisitions librarians often handled vendor relations and negotiations. ERM management tools have been introduced over the past decade and overall change in the area has slowed, giving libraries to opportunity to more efficiently adapt their organizational structures and workflows to the new environment. Libraries today often have a librarian or team of librarians directly responsible for ERM. Whereas reference and instruction positions may have once included ERM responsibilities, today, ERM positions may include some reference or instruction responsibilities. These inclusions have been declining however. In her study of ERM job announcements from 2000 to 2008, Murdock (2010) noted that “some responsibilities that were considered extraneous to e-resource specific tasks, such as reference service and cataloging, did show an overall declining trend”(p.39). Regardless of the type of library or area of librarianship in which a student may eventually work, she will likely engage in some way with the provision of electronic resources. While new librarians will have the opportunity to learn the specifics of their part in the ERM workflow while on the job, understanding how the work do impacts the work of their colleagues is essential for effective ERM. All students preparing for librarianship would benefit from understanding the big picture of ERM; LIS programs should offer a course dedicated to the subject. Many LIS programs cover this information in bits and pieces through a wide variety of courses, ranging from information organization to reference sources and services. Rarely do students gain knowledge about how the integration of those bits and pieces occurs in practice. This

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)0.002
metaresearch head score (Gemma)0.005
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: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.099
Threshold uncertainty score0.332

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0050.005
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0990.045

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 head0.008
GPT teacher head0.205
Teacher spread0.196 · 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
GenreMethods

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
Published2012
Admission routes1
Has abstractyes

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