Putting critical realism to use in ICT4D research: Reflections on practice
Bibliographic record
Abstract
Abstract This paper presents my reflections on adopting critical realist (CR) assumptions in information and communication technologies for development (ICT4D) research over the last decade. Critical realism, and its notion of generative mechanisms as contingent causality, offers one potential way to frame and engage in research that is compatible with the contextual nature of developing ICT4D theory. However, CR can be difficult to understand and operationalize. This paper provides practical insights on how to engage in ICT4D research underpinned by the CR philosophy of science. As such, the bulk of this paper presents insights and implications of accepting CR assumptions in the research process, looking at different elements of research (eg, including forming research questions, theory building, engaging in research). I use examples in ICT4D research to illustrate these implications.
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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 | 0.152 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.021 | 0.150 |
| Scholarly communication | 0.031 | 0.040 |
| Open science | 0.005 | 0.032 |
| Research integrity | 0.014 | 0.027 |
| Insufficient payload (model declined to judge) | 0.007 | 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".