International Initiatives and Regional Ecosystems for Supporting Artificial Intelligence Acculturation
Bibliographic record
Abstract
Abstract National and international initiatives to support AI education are discussed in this chapter. Following an examination of the various initiatives undertaken in OECD countries, the chapter highlights the House of Artificial Intelligence (MIA) activities supporting AI acculturation to the regional educational and industrial ecosystem in the French Rivera. The chapter delves into these achievements, detailing partnerships, educational outreach, entrepreneurship initiatives, and the nuanced approach to addressing gender biases in AI education. Through the different workshops, students are empowered to actively contribute to AI's evolution, transforming from consumers to creators. Gender perspectives are explored, tackling stereotypes and biases. The chapter concludes with a spotlight on the Smart Hive project, an interdisciplinary initiative fostering sustainable development through AI, exemplifying the MIA's role in creating a regional ecosystem for AI acculturation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".