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Record W3163870132 · doi:10.1080/02681102.2021.1920874

Frugal innovation and digital effectuation for development: the case of Lucia

2021· article· en· W3163870132 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInformation Technology for Development · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsEntrepreneurshipKnowledge managementBusinessResource (disambiguation)Scale (ratio)Business modelSociologyMarketingComputer science

Abstract

fetched live from OpenAlex

This paper illustrates how the lens of effectuation and frugal innovation can be employed to understand digital entrepreneurial practices in development contexts. It presents the case of the director Pawan Kumar, who produced the movie Lucia by relying upon digital tools to create a project identity, to access resources and knowledge from his network, to experiment with variations of his business idea, as well as to secure commitment from partners on a scale that would be impossible otherwise. Using this empirical setting, the paper analyses the practices employed by entrepreneurs in development contexts to overcome resource limitations and institutional voids by leveraging digital technologies to pursue opportunities. The case contributes to the literature on ICT4D by illustrating how digital entrepreneurship has the potential not only to bring about economic benefits, but also to stimulate local culture production, an impact of digital entrepreneurship often overlooked in the literature.

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.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.227
Teacher spread0.214 · 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