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Record W4413989991 · doi:10.33524/cjar.v25i2.769

Radical Incrementalism in Action Through Institutional Work: Case Studies of Embedded Research in South Africa

2025· article· en· W4413989991 on OpenAlexaffvenue
Mark Swilling, Ying-Syuan Huang, Nontsikelelo Mogosetsi-Gabriel, Alboricah Rathupetsane, Mlondi Ndovela, Priscilla Jezi, Garth Malan, Mapula Tshangela

Bibliographic record

VenueThe Canadian Journal of Action Research · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsMcGill University
FundersUniversiteit Stellenbosch
KeywordsIncrementalismAction researchWork (physics)Action (physics)Political scienceSociologyEngineering ethicsPedagogyEngineeringLaw

Abstract

fetched live from OpenAlex

Sustainability researchers and non-academic actors are working more closely than ever to drive deep structural transformation toward just and sustainable futures. This paper illustrates how embedded researchers in South Africa navigate the political nature of institutional spaces through transdisciplinary action research. We propose radical incrementalism as a guiding philosophy and analyse seven cases using a collaborative autoethnographic approach. The analysis identifies four strategic choices: whether to embed, how to engage critically, when to maintain distance, and how to sustain long-term commitment. These insights illuminate the ethical and political complexities of shaping—or failing to influence—policy change within government institutions.

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)0.023
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.121

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0250.035
Scholarly communication0.0100.009
Open science0.0030.019
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.849
GPT teacher head0.675
Teacher spread0.173 · 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 designQualitative
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

Citations1
Published2025
Admission routes2
Has abstractyes

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