The opportunities and value-adding activities of buy-back centres in South Africa's recycling industry: A value chain analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it