Transformed through the CARTA experience: changes reported by CARTA fellows about their PhD journey
Bibliographic record
Abstract
Transformative learning occurs when a person, group, or larger social unit encounters ideas that are at odds with their prevailing perspective. This discrepant perspective can lead to an examination of previously held beliefs, values, and assumptions. The Consortium for Advanced Research Training in Africa (CARTA) has since 2011 been training and supporting faculty from different African universities, to become more reflective and productive researchers, research leaders, educators, and change agents who will drive institutional changes in their institutions. As part of a mid-term evaluation of CARTA, an open-ended question was posed to the CARTA fellows asking them to describe any changes they had experienced in their professional lives as a result of the CARTA Programme. The 135 responses were inductively coded and analysed using qualitative thematic analysis. These themes were subsequently mapped onto Hoggan's typology of transformative learning outcomes. CARTA fellows reported shifts in their sense of self; worldviews; beliefs about the definition of knowledge, how it is constructed and evaluated; and changes in behaviour/practices and capacities. This paper argues that the changes described by the CARTA fellows reflect transformative learning that is embedded in CARTA's Theory of Change. The reported transformation was enabled by a curriculum intentionally designed to facilitate critical reflection, further exploration, and questioning, both formally and informally during the fellows' PhD journey with the support of CARTA facilitators. Documenting and disseminating these lessons provide a guide for future practice, and educators wishing to revitalise their PhD training may find it useful to review the CARTA PhD curriculum.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".