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Record W4386182285 · doi:10.1080/23299460.2023.2243122

Mobilizing capital for responsible innovation: the role of social finance in supporting innovative projects

2023· article· en· W4386182285 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Responsible Innovation · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCommunity Development and Social Impact
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsVenture capitalCorporate governancePortfolioBusinessImpact investingSet (abstract data type)Investment (military)Social responsibilityFinancePublic relationsPolitical scienceEmerging markets

Abstract

fetched live from OpenAlex

The literature on Responsible Innovation (RI) has not yet fully addressed the role played by social finance (SF) in supporting projects and organizations engaged in the production of innovations that tackle grand societal challenges. This study addresses this gap by empirically examining how SF investors select potential investees and the principles they judge important in SF. Our findings show that SF investors apply a combination of criteria to select investment projects where entrepreneurial motivations, environmental, social and governance commitments, and the nature of the impacts being generated align with their portfolio's mission. Though not all SF investors in our sample had knowledge about the concept of responsibility, they nonetheless mobilized a broad set of principles that are closely aligned with the aims and practices of RI. More research is needed to clarify the type of resources SF use to support RI and the conditions under which these resources are provided.

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)0.008
metaresearch head score (Gemma)0.005
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: Empirical
Teacher disagreement score0.300
Threshold uncertainty score0.667

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.014
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 head0.072
GPT teacher head0.323
Teacher spread0.251 · 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