Transforming Innovation Systems for Sustainable Development Challenges: A Latin American Perspective
Bibliographic record
Abstract
Abstract Sustainable development challenges have been gaining increased attention from scholars across a wide range of disciplines and governments and business leaders of developed and developing countries. In this chapter, we present selected Latin American socioeconomic indicators, and we note that much progress is needed to achieve the region's many sustainable development goals. We bring forth contributions from different streams of innovation studies for addressing grand challenges, and we discourse on how they push the sustainable development mandate forward. Innovation scholars have highlighted the need to elaborate novel transformational approaches to innovation for addressing such pressing grand challenges. Some scholars have also proposed that while the innovation systems framework is well-suited for addressing sustainable development challenges, it must first be profoundly and radically transformed to account for the novel ways of innovating and integrating a diversity of systemic economic actors and social stakeholders who have conflicting visions, interests, norms, and expectations. We present the different foundational strengths and weaknesses of the innovation systems framework and we discuss the pertinence for its profound and radical transformation. We conclude by organizing these different, yet complementary views of innovation in a conceptual framework while discussing the implications for Latin America and future research.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".