MétaCan
Menu
Back to cohort
Record W1495777512 · doi:10.1002/meet.2014.14505101110

Critical making with a raspberry pi ‐ towards a conceptualization of librarians as makers

2014· article· en· W1495777512 on OpenAlexaff
Krista E. Parham, Anna Ferri, Stephanie Fan, Matthew Murray, Rebecca A. Lahr, Ekatarina Grguric, Monica Swamiraj, Eric M. Meyers

Bibliographic record

VenueProceedings of the American Society for Information Science and Technology · 2014
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsConceptualizationCreativityProcess (computing)IntrospectionRaspberry piSociologyComputer scienceIntersection (aeronautics)SPARK (programming language)World Wide WebKnowledge managementPsychologyEngineeringInternet of ThingsSocial psychology

Abstract

fetched live from OpenAlex

ABSTRACT Makerspaces, designated spaces to foster creativity and technology skills, are increasingly being incorporated into libraries. Although makerspaces in libraries are depicted positively in the literature and are praised by professional organizations, there is little exploration of the acculturation that results as libraries and makerspaces learn to coexist. In keeping with Matt Ratto's model of “critical making,” we used the process of collaboratively building an interactive Readers’ Advisory Device (RAD) that runs on a Raspberry Pi computer to elicit introspection. In this poster we describe how our interdisciplinary group faced challenges working with unfamiliar tools and technology through a non‐hierarchical, collaborative, and iterative process, seeking knowledge and skills from the maker community. We then engaged the wider community around both how and why we developed this device by exhibiting it at a Maker Faire. Our experience taught us about the making process and helped us think critically about the intersection of libraries and makerspace cultural values. We found that making is as much an act of networking as of creation.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.806
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0000.005
Scholarly communication0.0000.003
Open science0.0010.000
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 head0.011
GPT teacher head0.282
Teacher spread0.271 · 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.

Study designTheoretical or conceptual
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

Citations5
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the American Society for Information Science and TechnologySame topicInnovative Human-Technology InteractionFrench-language works237,207