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Transforming Innovation Systems for Sustainable Development Challenges: A Latin American Perspective

2022· book-chapter· en· W4282838945 on OpenAlexaff
Claudia De Fuentes, Jahan Ara Peerally

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsGenetec (Canada)St. Mary's University
Fundersnot available
KeywordsLatin AmericansPerspective (graphical)Sustainable developmentPolitical scienceRegional scienceGeographyComputer science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.036
GPT teacher head0.239
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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