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Record W2283617157

YA Q&A Collaboration

2010· article· en· W2283617157 on OpenAlexaboutno aff
Stephanie A. Squicciarini, Maureen Hartman, Erica Cuyugan

Bibliographic record

VenueYoung Adult Library Services · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsnot available
Fundersnot available
KeywordsGeneral partnershipLibrary sciencePublic relationsWork (physics)Political scienceSociologyMedical educationManagementEngineeringMedicine
DOInot available

Abstract

fetched live from OpenAlex

Collaborating can be hard work-and very rewarding. YALS asked three experienced YALSA members and collaborators to share some of their tips and tricks. YALS: What is your experience with collaborating with other agencies? Maureen Hartman In my previous job as Partnerships Coordinator with the Minneapolis Public Library, it was my responsibility to coordinate strategic partnerships for the library on behalf of youth. These collaborations ranged from small and focused--promoting National Mentoring Month in the library and on the website--to larger and more complex, including working with the local schools and parks on a citywide summer reading program. Some of the most exciting collaborations have been for and with youth, including making the library the home of the Minneapolis Youth Congress and working with the Minnesota Historical Society and the University of Minnesota on History Day Hullabaloo, a library event connecting students to library resources to complete their History Day projects. Erica Cuyugan Partnership and collaboration are a great way to implement large-scale programs and ideas. I love partnering with librarians, teachers, community organizations, and other city staff members on programs. For the past five years, I have successfully collaborated with youth advocates from our city's Cultural and Human Services departments on the Annual Santa Monica Teen Film Festival program. This program has grown in size and scope since the first year, when we received about forty submissions and screened eleven films in one afternoon. This year (our fifth year), we screened thirty-three films out of a record 180 submissions for the festival over two days--one evening and one afternoon screening. Films now come from all over the country and Canada. Stephanie Squicciarini The public library I work in is a school district library, meaning our budget is voted on directly by residents of the school district. So we have a natural and close relationship with the schools and collaborate on many different projects. I also worked with a local juvenile detention facility after being awarded a Great Stories Club Grant. This collaboration included MLS students from a nearby college completing internship hours with the facility. But the longest lasting and most diverse collaborative endeavor has been the Greater Rochester Teen Book Festival (TBF) that I founded seven years ago. Over the years, TBF has been made possible through the work of school and public librarians in three different counties. For the past two years, it has included a partnership developed with a college and its education program. These cooperative and collaborative layers work throughout the year to create an event connecting teens and authors. YALS: What are some of the dos and don'ts of collaboration that you have learned over the years? Maureen Hartman Do make sure the library is getting something from the collaboration. Libraries and librarians are often so willing to help that we forget that both parties have to get something out of any good collaborations or partnerships--otherwise they won't sustain in the long term. Focus on one thing at a time. Sometimes when we meet a new potential collaborator, we get really excited, have a million ideas, and launch them right away. Pick the one that makes the most sense and the one your library can support the most easily. On the basis of its successes and failures, you'll learn a lot about what you can expect for future collaborations with that organization. Don't go it alone. Remember the library's strategic priorities. Without the support for your collaboration--either from your direct supervisor or from the system at large--you'll have a hard time sustaining it. Even the best, most natural idea, if it's not the direction your library can support, will end up pretty frustrating. Don't limit yourself to the tried-and-true. …

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)0.025
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.439
Threshold uncertainty score0.801

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.004
Science and technology studies0.0160.005
Scholarly communication0.0300.020
Open science0.0040.034
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.4390.249

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 head0.006
GPT teacher head0.258
Teacher spread0.252 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2010
Admission routes1
Has abstractyes

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