A story of social entrepreneurship through the creation of the Hopeboots project
Bibliographic record
Abstract
Research methodology This case was developed by using primary data collected from two separate one on one interviews, a panel interview in which Josee was featured and secondary data collected from news articles and publications featuring Josee. Information specific to Atikuss’ offerings was found through the Atikuss website. A translation software was used to understand many of the articles about Josee, as many were in French. Case overview/synopsis Atikuss (meaning young caribou in Innu) is the sustainable business founded by Josee LeBlanc, an Indigenous woman from Northern Quebec. As a workshop-boutique, Attikuss offers a diverse selection of hand-made traditional Indigenous items from her own Indigenous culture. Hopeboots is a project run through Atikuss which allows customers to create their own Mukluks while learning about Indigenous culture and the story behind every design. When starting her business, Josee learned that the women making mukluk boots were not earning a livable wage for their work. Her dilemma when creating a sustainable business was whether to increase the beaders wages to a fair wage, costing her and the consumer more, or maintaining the status quo by continuing to pay the beaders less then five dollars an hour. Josee’s decision to increase wages generated opportunities and increased well being through social investments in her community. This decision considers the cost to many stakeholders and offers an Indigenized perspective to entrepreneurship. This case is relevant to Indigenous entrepreneurship, sustainability, social innovation, business ethics, and corporate social responsibility. Complexity academic level This case is targeted toward university-level students and can be relevant to graduate-level students as well.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".