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Record W3101007542 · doi:10.22215/etd/2020-14175

The Design of Local Ecosystems within a Global Technology Entrepreneurship Challenge

2020· dissertation· en· W3101007542 on OpenAlexaffabout
Jasmine Shaw

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsCarleton University
Fundersnot available
KeywordsGlocalizationEcosystemBusiness ecosystemEntrepreneurshipContext (archaeology)HierarchyAdaptation (eye)Knowledge managementEnvironmental resource managementArchitectureBusinessGeographyGlobalizationRegional scienceComputer sciencePolitical scienceEcologyEconomics

Abstract

fetched live from OpenAlex

Global organizations, often part of a business ecosystem, are continuously striving to expand their reach in local communities, but they face significant adoption challenges. These multilevel systems have a natural hierarchy, which can be analyzed from a design perspective. Leveraging constructs from design science, glocalization, and business ecosystems, this research examines Technovation, the world's largest technology entrepreneurship program for girls. Through an embedded multiple-case study, Technovation is described using ecosystem frameworks, then a cross-case analysis of six chapters from Canada and Mexico identifies similarities and differences, and global ecosystem requirements are specified as design rules. There are three key findings. First, local ecosystems have a different architecture than the global ecosystem. Second, chapters are influenced by passive and active forces. Third, design rules create boundaries for local adaptation of global components. These insights will assist managers, ecosystem designers, and Technovation practitioners navigate the complex, global context in which they operate.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.927
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

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

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 head0.029
GPT teacher head0.242
Teacher spread0.213 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations0
Published2020
Admission routes2
Has abstractyes

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