Makerspaces – A Catalyst for Levelling-Up the UK and Skills Development in STEM Subjects: A Sentiment Analysis
Bibliographic record
Abstract
Makerspaces offer the general population access to traditional fabrication equipment (e.g. welding machines, woodworking tools) and modern technology (e.g. CNC machines, 3D printers). Many researchers have suggested that makerspaces can be an essential catalyst for distributed manufacturing, the circular economy, community building projects and education of skills. By conducting a sentiment analysis, this study investigates whether the maker community agrees with the of ten-suggested benefits. The study uses over 5,000 reviews from the UK, USA, Canada, Australia, and New Zealand. Of these reviews, 801 were about makerspaces within the UK. The results highlight the positive attitude in both rural and urban environments towards makerspaces. In terms of the UK's much publicized Levelling Up agenda, makerspaces can enhance education, broaden access to both traditional and modern technologies as well as offering opportunities for building communities and social interactions. Using makerspaces for business purposes is not frequently discussed. However, makers do value distributed manufacturing technologies (i.e. 3D printers, CNC machines, digital technologies), which enables them to fabricate unique items for personal use. The opportunity for a circular economy is rarely mentioned in the reviews.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".