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Record W4385566346 · doi:10.1016/j.ijis.2023.08.003

Shifting the paradigm: A critical review of social innovation literature

2023· review· en· W4385566346 on OpenAlexaff
Amy Phillips, Rosalie Luo, Joel Wendland-Liu

Bibliographic record

VenueInternational Journal of Innovation Studies · 2023
Typereview
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsWestern University
Fundersnot available
KeywordsInstrumentalismScholarshipField (mathematics)SociologyStakeholderDiversity (politics)DemocracySocial scienceEpistemologyPolitical sciencePublic relationsPolitics

Abstract

fetched live from OpenAlex

In this review of ten years of social innovation research (2012-2022), we define and explore three paradigms in the field: instrumentalist, strong, and democratic. We investigate how language usage and geography play a central role in identifying which paradigms recently published scholarship falls into. While we do not insist that sharp divisions exist between each paradigm, we do find that on the “instrumentalist” side, language tends to abstract or neutralize power relations. Further, these perspectives tend to derive from Western or Eurocentric orientations or biases. The “strong” paradigm accepts the necessity of institutional and stakeholder engagement and seeks to engage socially excluded populations. In contrast, geographical diversity, attendance to historicized and systemic inequalities, and elevation of the most marginalized communities are more likely to be centered in the “democratic” paradigm. We apply this discussion to recent research in arts-related social innovation and the related field of social entrepreneurship.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0180.017
Science and technology studies0.0020.004
Scholarly communication0.0050.008
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.370
GPT teacher head0.524
Teacher spread0.154 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

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

Citations26
Published2023
Admission routes1
Has abstractyes

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