Designing Safe Learning Environments for Discovery, Empathy, Failure, and Igniting Impact in Latin America
Bibliographic record
Abstract
ObjectivesInnovation performance is commonly understood as resulting from strategy, process and decision-making under uncertainty.We analyze the significance of procedural and behavioral factors in shaping learning environments for innovation in teams where it's safe to explore, experiment and invent.We study the learning traces linking emotional, cognitive, and technical performance in learning environments under frustration, uncertainty and perceived risk.2 Theoretical framework Generating a rich and efficient learning experience on innovation requires a systemic approach.We combine multiple perspectives for enabling meaningful innovation learning (Ausubel, Novak et al. 1968, Diaz Barriga andHernández 2002) through a process-oriented approach (Vermunt 1995) that includes selfregulation and external regulation, sense-making and sense-giving (Gioia and Chittipeddi 1991, Albon and Jewels 2007) led mostly by team members.We explore how mediated sense-making (Strike and Rerup 2016), situated cognition (Brown, Collins et al. 1989), creativity (Sawyer 2006, Sawyer 2012), contextual inquiry (Holtzblatt and Beyer, 2017), and trans-disciplinary processes (Vilsmaier, Engbers et al. 2015) affect learning environments in a positive way.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".