Industry competencies or wellbeing capabilities? Assessing a Kenyan case of CBET reform
Bibliographic record
Abstract
Technical and vocational education and training (TVET) is increasingly viewed as a development strategy in Africa. Yet TVET is also criticized for its human capital orientation focused primarily on market needs. Competency-based education and training (CBET), a TVET approach, has been further criticized for reducing students to mere instruments of value creation defined by the competencies required by industry. This study explores whether CBET can be reframed to incorporate a capability approach that better promotes wellbeing in Africa beyond industry competencies. The capability approach is a wellbeing framework that emphasizes expanding the agency and capabilities of people to choose the kinds of lives they find valuable. Incorporating a capability approach orientation into CBET would place fostering student agency and wellbeing capabilities as a central educational focus rather than just the needs of industry. Using the Most Significant Change method, the study assesses a case of CBET reform in Kenya for its potential to expand student agency and capabilities. The findings illustrate that the adoption of CBET’s learner-centred pedagogy played a role in expanding student capabilities while also building industry-relevant competencies. Moreover, intentionally mainstreaming gender within CBET further expanded female agency and capabilities. Yet, this expansion of wellbeing is limited by the financial character of CBET and its interconnection to student poverty. Overall, the findings demonstrate that there is potential for CBET to move beyond a sole focus on human capital and embrace expanding student capabilities. [Abstract continued in PDF]
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".