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Record W2596246102 · doi:10.18260/1-2--5613

Teaming With Possibilities: Working Together To Engage With Engineering Faculty And Students

2020· article· en· W2596246102 on OpenAlexaff
Jan Fransen, Jon Jeffryes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Education and Pedagogy
Canadian institutionsDominican University College
Fundersnot available
KeywordsCurriculumLibrary scienceMedical educationSociologyPsychologyComputer sciencePedagogyMedicine

Abstract

fetched live from OpenAlex

Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Teaming with Possibilities: Working Together to Engage with Engineering Faculty and Students Last summer two of us moved into a shared office, starting our new positions as engineering librarians at the University of Minnesota. We support four engineering departments that total more than 100 full-time faculty, nearly 500 graduate students, and well over 1000 undergraduates. Janet Fransen has an undergraduate degree in engineering and was beginning her first library position after 20 years working in the technology sector. Jon Jeffryes has a background in the humanities and two years of professional library experience at the engineering library of another university. But neither of us had experience as a liaison librarian. When we started our jobs, we found ourselves sifting through the long list of duties in our job descriptions, contemplating just where to begin. As newly-minted librarians, we looked to the literature as well as our fellow liaisons for guidance. The job of a liaison librarian is busy and multi-faceted. The Reference and User Services Association division of the American Library Association includes expectations ranging from formal activities—"surveys of library users, faculty, staff and students to evaluate their satisfaction with library resources; regular meetings with faculty to ascertain planned curriculum developments and to identify new resources; communication of available materials and services; and establishment of a process by which library users can suggest purchases"—to the informal "participation in campus organizations and activities, monitoring campus media for activities and events that affect collections, and encouraging library use and support by nonusers."1 As if these static lists weren't enough, the liaison's role continues to evolve. As Frank, et al. posit, "the changing nature of scholarly communication and inquiry requires a more dynamic, communicative, and customized approach."2 As the needs of our users change with the times, the trend in liaison librarianship is a move toward more time-intensive, personalized services. With such a wide variety of activities ahead of us and limited hours in the day to test all possible methods, we decided to make the most of our differing strengths and experiences and formed a team approach to meeting the information needs of our engineering audience. Both overachievers, we tend to want to do it all ourselves. But between the numbers and the quickly changing landscape, we saw that we could be more effective if we worked together. So together we’re reaching out to our users through instruction, scholarly communication, and—of course—marketing. Katzenbach and Smith's definition of team has helped us step back and learn from each other's experiences: "a small number of people with complementary skills who are committed to a common purpose, performance goals, and approach for which they hold themselves mutually accountable."3 Echoing Baughman's findings at the University of Maryland, we hoped that our team would "bring together a broad range and mix of individuals' skills in a collective way to support problem solving."4 Marketing Before we started our jobs, our academic departments had, naturally, worked with other librarians. Our predecessors had formed relationships with their departments and particular faculty members. In some cases, we were able to pick up where they left off. But we've found

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

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

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 head0.026
GPT teacher head0.255
Teacher spread0.229 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2020
Admission routes1
Has abstractyes

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