Challenges and Opportunities for the Textile Industry in Ghana: A Study of the Adinkra Textile Sub-Sector
Bibliographic record
Abstract
The purpose of the study is to examine the challenges facing the Ghanaian textile industry with emphasis on the Adinkra textile cloth printing sub sector in Ghana, as well as opportunities for improving the industry. The sub sector is distinct and has different dynamics from other sub sectors of the broader textile industry. The study informs policy makers and private sector on the factors resulting in the decline of the Adinkra textile sub sector and the need to sustain the sub sector of the textiles industry due to its heritage importance and contribution to the economy. It also examines potential business opportunities for local and international firms to invest in the textile sub market for expansion and profitability. An empirical research design with mixed-methods approach was used in this study. A qualitative approach (semi-structured interviews) was used to collect the data, coded and analysed using standard NVivo software which generated quantitative outcomes for descriptive statistical analysis. Qualitative approach was also used to interpret the findings of the study. The results indicated that, factors such as lack of access to capital, outmoded forms of technology, issues with supply chain and nature of the dyes for printing the textile cloths are primarily responsible for the declining state of the sub sector. The implications of the challenges and the declining state of the sub sector are discussed and solutions proffered to solve the challenges.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".