Democratizing the Maker Movement: A Case Study of One Public Library System’s Makerspace Program
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.036 | 0.013 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".