Adult and Continuing Education in Kenya: The Need for Transformative Leadership
Bibliographic record
Abstract
As a multidisciplinary process, Adult and Continuing Education (ACE) is meant to foster efficient lifelong learningthroughout life. To achieve this, it should be geared towards providing knowledge, skills as well as attitudes thatensure equal opportunities to life, meeting the needs of individuals of all ages so as to understand the world and thechanges being experienced. In a bid to attain a newly industrialized status, Kenya’s Vision 2030 development planendeavours to develop the relevant human resource base through, among others, ACE. However, the provision ofACE in Kenya faces such challenges as inappropriate learning environment, inadequate instructional materials andthe inability to balance career and family demands. This is partly due to inept leadership, non-adherence to therelevant policy guidelines and pieces of legislation. In this paper, it is asserted that in order to achieve the aims ofACE, embracing transformative leadership is paramount. Thus, this piece of work is informed by the theory oftransformational leadership which lays emphasis on how leaders, in various fields, can actually impact and ortransform their subjects positively, and vice versa. In addition to exploring the policy and legislative frameworksconcerning the provision of ACE in Kenya, this paper provides some intervention measures to the outstandingchallenges.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".