Bibliographic record
Abstract
The African European Maker Innovation Ecosystem (mAkE) project, funded under the EU Horizon 2020 programme, ran from 2022 to 2025 and focused on fostering innovation, sustainability, and socio-economic growth through makerspaces and other hardware-focused Digital Innovation Hubs (DIHs) in Africa and Europe. In three years, the initiative made significant strides in supporting entrepreneurship, capacity building, and sustainable practices in makerspaces/DIHs while establishing makerspaces as vital components of local and global innovation ecosystems. The mAkE project leaves a legacy as a catalyst for sustainable innovation and socioeconomic development. By fostering grassroots creativity, building global partnerships, and promoting policy alignment, the project has laid a robust foundation for continued growth and impact. The tools, resources, and networks established through this initiative stand as a model for empowering makerspaces/DIHs worldwide, driving inclusive innovation and resilience in local and global economies. This document provides the Final Impact Assessment Report for the mAkE project and higlights its achievements.
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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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.018 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.214 | 0.141 |
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".