Stitching sustainability: an autoethnographic exploration of fashion practices in western Newfoundland and southwest Nigeria
Bibliographic record
Abstract
This thesis investigates sustainable fashion through an autoethnographic lens, drawing from the researcher's personal journey as a fashion entrepreneur in Nigeria and an immigrant entrepreneur in Newfoundland and Labrador, Canada. The central research questions ask first: How can sustainable fashion practices be adapted to Newfoundland's cultural, environmental, and economic context? And second: how can insights from Southwest Nigeria's sustainability models inform this adaptation? Anchored in comparative and reflexive inquiry, the study aims to (1) analyse the cultural and market dynamics influencing sustainable fashion in both regions, (2) evaluate opportunities and barriers in practice, policy, and perception, and (3) propose context-sensitive, community-driven fashion interventions. Hypotheses tested include: that culturally rooted sustainable fashion is more readily accepted by local communities, and that this can strengthen community engagement and cultural preservation in Newfoundland. Methodologically, the research integrates autoethnography with narrative analysis, participant observation, reflexive journaling, and thematic analysis. The analysis is framed by five emergent themes: fashion entrepreneurship, social significance of fashion, circular fashion, consumer behavior, and cultural perspectives. The thesis documents the founding of Bimpegold, a sustainable fashion initiative in Newfoundland, shaped by the researcher's Nigerian experiences and adapted to a setting with an aging population, limited tailoring culture, and abundant institutional support. Findings reveal that while Nigeria has a vibrant tailoring culture but limited systemic support, Newfoundland offers structured opportunities (e.g., funding, incubators) but suffers from fashion illiteracy and excessive clothing waste. The research demonstrates that sustainable fashion must be place-based and relational, grounded in cultural pride, economic resilience, and environmental responsibility. It concludes with recommendations for fashion education, policy support, and curriculum development, particularly for K−12 systems, positioning sustainable fashion not only as a movement, but as a model for ecological and social renewal
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".