The opportunities and value-adding activities of buy-back centres in South Africa's recycling industry: A value chain analysis
Bibliographic record
Abstract
This paper investigates the entrepreneurial opportunities and value-adding activities of buy-back centres in the recycling industry. Using Porter’s firm-level value chain framework as theoretical framework for this analysis, a concurrent mixed method design was used to collect information from 67 buy-back centres across South Africa by means of face-to-face interviews, accompanied with a questionnaire with open-ended and close-ended questions. Buy-back centres’ competitive advantage is that they have the facilities to add value to the recyclables according to the recycling industry’s standards and specifications. To be viable, they need to attract large and sustainable volumes of recyclables, which often poses a challenge. Increased volumes of recyclables can translate into more jobs and income earning opportunities at all hierarchical levels in the recycling industry. A recycling model that increases the volumes of recyclables recovered by buy-back centres through informal sector activities is proposed. Such a model should facilitate changing citizen behaviour and implementation of, among others, responsible separation at source programmes to increase the volumes of cleaner recyclables. Increased supplies of recyclables should, however, be accompanied by an increase in the demand for products made from recyclables, to absorb the increased supply.
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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 | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".