Social Challenges Learners Residing in Informal Settlements in Katima Mulilo Town Face in Learning
Bibliographic record
Abstract
The purpose of this article was to find out social challenges learners residing in informal settlements in Katima Mulilo Town face. Informal settlements crop up as people move from rural settings to urban areas as they seek better facilities, a process known as urbanisation. However, not all who migrate into urban areas end up getting the required facilities. This result in some finding residence in informal settlements where conditions are deplorable and as a result brings some social challenges to learning. This article’s objective is to explore the social challenges learners residing in informal settlements face in learning. The article also seeks possible ways to deal with the social challenges in order to make learning possible. To come up with response to the questions, the study adopted a qualitative research approach. Instruments used to generate data were observation and interviews. To support the data generated from the participants, the learners, the social capital theory and urban theories were used. Some of the social challenges found to impede learning include poverty, flooding, expensive water and electricity bills, limited sewage disposal system, unfair relocations, poor sanitation, unemployment and high crime rate. Solutions were also suggested on how to overcome these challenges.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".