Bibliographic record
Abstract
Flourish and Grow (F&G) was a sole proprietorship based out of London, Ontario, retailing handmade fine art, jewelry, and apparel that conveyed a narrative of the founder’s Indigenous Mi’kmaq culture and values. During the COVID-19 pandemic, Mikaila Stevens, founder of F&G, had ample time to spend at home and developed a newfound passion for beading and creating art as a way to connect with her Indigenous Mi’kmaq culture and values. In the next year, she created a collection of fine art products to expand her business. After obtaining government grants, Stevens was able to expand F&G and forge connections with her customers through her expression and personal creations. Stevens sought to explore her Mi’kmaq ancestry and had a strong desire to create unique introspective pieces that showcased techniques from her heritage. Reflecting on the next steps for F&G, Stevens recognized the urgent need to optimize the product, pricing, promotion and distribution of her sole proprietorship to remain competitive and increase revenue to sustain full-time F&G employment and continue her work of passion. Where should she start?
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.010 |
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".