Bibliographic record
Abstract
The keynote presentation at the 2023 Cando Annual Conference focused on telling the story of the acquisition of Clearwater Seafoods by the Mi'kmaw Consortium and Premium Brand Seafoods. It highlighted a Mi’kmaw corporate perspective of the acquisition, co-presented Mike McIntyre, Membertou CFO, Jenny Morgan, Clearwater Seafoods LP, Darryl McDonald, CAO Paqtnkek Mi'kmaw Nation. This lesson from experience submission is a summary of their presentation which focused on three core aspects of the deal. First was the messaging around building business relationships. The endeavor was only possible because Membertou had a reputation for being community minded, trustworthy, and professional. Second, the deal was crafted to minimize risk to pre-existing community assets the deal would not hurt the borrowing potential of the coalition communities in the near or distant future. Finally, the presentation highlighted the opportunities for Clearwater to expand through strategic innovation. Although Premium Brands was not represented in the presentation, the explanation of their role in the partnership highlights the potential for strategic innovation in the future.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".