Bibliographic record
Abstract
This Interim Impact Assessment Report provides an overview of the main outputs, initial outcomes, and important lessons learnt from the mAkE project up to the end of Y2 (end-January 2024), and of the potential future sources of impact assessment data in in project year three (Y3) from February 2024 to January 2025. It can be seen, across the core chapters of this Interim Impact Assessment Report, that the mAkE project has, through its first two years of work, successfully commenced delivery, in collaboration with its stakeholders, a broad portfolio of programmes. These programmes are grounded in the needs of stakeholders, with those needs established collaboratively through numerous means, including in-depth stakeholder interviews, stakeholder focus group discussions (FGDs), online discussion channels, Community Call and Business Model webinars, and engagement with stakeholders at numerous events, in both Africa and Europe, devoted to fostering sustainable Digital Innovation Hubs (DIHs)/makerspaces on both continents.
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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 | 0.034 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.135 | 0.072 |
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".