Propuesta de entrada al mercado canadiense para UMA Perú: Apoyando ingresos estables de mujeres artesanas y fortaleciendo la confianza del consumidor a través del canal digital
Bibliographic record
Abstract
UMA Perú, founded in 2019, is a Peruvian social enterprise with the purpose of supporting women artisans from marginalized rural Andean communities by providing market access, fair income and cultural recognition. As these women face numerous challenges including low compensation for their work, reliance on middlemen and a lack of visibility, UMA supports them by connecting them with socially conscious customer. This helps them in preserving their cultural heritage and be economically independent. As UMA plans to expand further in North America by entering the Canadian market, adapting to local customer needs is essential. Research and interviews revealed that Canadian shoppers value authenticity and prefer in-person purchases, as online platforms create insecurities about product origin and social impact. To address this, two user groups representing the artisan women on the one, and the socially conscious customer on the other side were analyzed to reveal their needs. Where artisan women seek fair compensation, independence and respect for her culture, the Canadian customer seeks transparency and evidence for ethical claims. Based on this, the proposed solution includes the implementation of a digital landing page that enables transparent communication about product origin, artisan storytelling and impact measurement. With the integration of a physical product card added to each purchase, that leads to this landing page through a QR Code, customer trust and artisan recognition can be enhanced. This solution builds on UMA’s existing digital infrastructure and can scale across markets, while being financially and socially viable and enhancing customer trust and ethical consumption.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".