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Record W2981746745 · doi:10.5860/rusq.58.4.7150

Democratizing the Maker Movement: A Case Study of One Public Library System’s Makerspace Program

2019· article· en· W2981746745 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueReference & User Services Quarterly · 2019
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyConstruct (python library)Public relationsDemocracyMovement (music)Computer sciencePolitical scienceLawPoliticsAesthetics

Abstract

fetched live from OpenAlex

The maker movement has found a home in public libraries. Field leaders including public libraries in Chicago, Chattanooga, Houston, Louisville, and Toronto have built robust makerspaces, developed maker programming for a diverse range of patrons, connected community experts with library users for the purpose of sharing information, and fostered communities of practice. Characterized by open exploration, intrinsic interest, and creative ideation, the maker movement can be broadly defined as participation in the creative production of physical and digital artifacts in people’s day-to-day lives. The maker movement employs a do-it-yourself orientation toward a range of disciplines, including robotics, woodworking, textiles, and electronics. But the maker ethos also includes a do-it-with-others approach, valuing collaboration, distributed expertise, and open workspaces. To many in the library profession, the values ingrained in the maker movement seem to be shared with the aims and goals of public libraries. However, critiques of the maker movement raise questions about current iterations of makerspaces across settings. This article highlights critiques and responses regarding the “democratic” nature of the maker movement, and in particular, the article analyzes ways librarians involved in a prominent public library maker program discursively construct making and maker programming in relation to the maker movement more generally.

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.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.645
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0020.000
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 head0.025
GPT teacher head0.263
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