Integrating a Community Library into the Teaching and Learning Programme of Local Schools
Bibliographic record
Abstract
According to estimates by Statistics South Africa, only 33% of learners in the Limpopo Province (one of the nine Provinces in South Africa) have access to a functional school library or media centre. This has been regarded as one of the main factors which have contributed to the Province consistently producing one of the lowest pass rates in comparison to its counterparts. While there was enthusiasm amongst some teachers that the establishment of Seshego Community Library would bring some relief to educators starved of a functional library service in their schools, some teachers and learners were not as enthusiastic and receptive to the Community Library. This paper explores some of the barriers inherent in introducing a library to a community which was not previously exposed to, and accustomed to making use of its services, and making it an integral part of the teaching and learning programme. Issues of resistance to the community library’s outreach programme, largely emanating from lack of motivation and a low morale amongst some teachers and principals alike, as well as an erosion in the culture of teaching and learning, are explored. There is a need to break down the existing barriers to encourage teachers and learners to make use of the Community Library’s services and facilities to add value to their teaching and learning endeavours.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".