Learners' views on access to drugs and deterrent value of present measures
Bibliographic record
Abstract
This is an overview of a school safety project with the general aim of assisting relevant stakeholders to explore a problem-solving strategy for creating a safer schooling environment and one that is more conducive to a culture of teaching and learning. A more specific aim of this survey was to investigate how accessible drugs are to learners. Given the detrimental effects drugs have in society and using the research group's responses, one of the objectives was to determine the extent of current deterrent measures. The research sample comprised of 1988 learners from 35 primary and secondary schools in District 4 in the Pretoria school area. Most (56, 7%) of the respondents were female, compared with 43, 3 percent male. Learners younger than 17 years of age constituted 54 percent of the research group, while those aged 17 years and older represented 46 percent. Approximately 5 percent (5, 4%) of the respondents were from primary schools and 94, 6 percent from secondary schools. As regards population group, the majority of the respondents were black (41, 7%), followed by whites (37, 9%), Indians (10, 7%) and coloureds (9, 7%). The results revealed significant levels of difference between the respondents' perceptions and attitudes. The findings of this study indicated that 40 percent of the learners can buy drugs, such as dagga, within a few hours or less, while 30, 1 percent indicated that they can buy dagga within 30 minutes or less. Approximately one quarter (22, 1%) of learners indicated that they can buy hard drugs such as LSD, ecstasy, cocaine or heroin within a few hours. Furthermore, one quarter indicated that they know of learners at their school who sell illegal drugs, while 26, 7 percent have actually seen illegal drugs being sold at their schools or inside the school grounds. Another 42, 2 percent of the learners has also personally observed the sale of illegal drugs in their neighbourhood. Learners older than 16 years of age seem to be more familiar with the illegal drug market and its culture, and appear to have better access to drugs than their younger counterparts. Male respondents appear to be more knowledgeable than female learners about illegal drug activities. Coloured respondents are constantly over represented compared with the other population groups. The research findings on the extent to which drugs are accessible to learners obviously warrant a national follow-up research project.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".