Literature Review on Integral, Mixed, and Creative Research Approaches for Complementary Currency Impact Evaluation
Bibliographic record
Abstract
According to the integrative review of all 194 articles published in the International Journal of Community Currency Research from 1997 to 2022, two-third (66.0%) were dealing with currency impact assessment—compared to one-sixth (17.9%) for 4 other literature reviews since impact’s definition influences its outcome—of which two-fifth (38.3%) had a positive impact but five-ninth (55.5%) had a neutral impact for a positive/negative impact ratio of 6.1. On average, the existing currencies studied represented a multiplier effect or velocity of 12.1, 7.33% of a targeted population, and 3.01% of a monetary mass or gross domestic product—and only one-fourth (23.6%) became inactive after 7 years. As suspected, seven-eighth (87.6%) investigated currencies with sustainable development objectives—involving 2.87 of the 5 pillars of sustainable development and targeting 5.26 of the 17 Sustainable Development Goals. Coming from 19 different disciplines, authors used 3.66 methodologies in average—among 79 different methodologies including the usual systems theory and econometrics—but four-fifth (78.4%) unexpectedly used multi-methodological frameworks (Integral Methodological Pluralism, ‘mixed methods’ research, ‘creative research’ methods) whereas only two-fifth (39.2%) used meta-theoretical paradigms (Complex Thought, Integral Theory, Critical Realism). Therefore, meta-theoretical paradigms with multi-methodological frameworks for the study of money or currency as a complex phenomenon are recommended.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.066 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.032 | 0.031 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".