Killing Two Birds With One Stone? A New Pragmatist Perspective on the Rigor‐Relevance Gap in the Literature on Capacity Building Projects
Bibliographic record
Abstract
ABSTRACT Despite scholarly and professional efforts, most capacity building projects fall short. The normative agenda, an effort to structure the concept and its practical applications, failed to produce rigorous thinking. A rigor‐relevance gap seems to prevail. This article examines capacity building in the academic literature using criteria of scientific rigor and practical relevance. We asked: Why has the normative agenda failed to produce rigorous capacity building thinking? What can be done to improve the normative agenda for successful outcomes in the field? We analyzed 72 peer‐reviewed articles through content analysis. We found that 71% of these articles are relevant to practice. However, while 52.5% involve case‐studies, 94.5% of the articles lack rigor, as they are not explicit about or provide no methodology. We thus advocate for a scholarly literature based on the new pragmatism as an epistemological framework to bridge the rigor‐relevance gap by promoting contextual sensitivity and methodological pluralism in research.
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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 | 0.324 | 0.353 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.009 | 0.141 |
| Scholarly communication | 0.035 | 0.055 |
| Open science | 0.007 | 0.020 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".