Bibliographic record
Abstract
Outstanding examples of economic development in Aboriginal communities across the country are reported in a variety of documents, but most extensively and consistently in the Journal of Aboriginal Economic Development.In this issue, we highlight a variety of activities that extend from British Columbia to Ontario, and with several examples from Manitoba in particular.The words of those who are involved in these ventures are captured for anyone to learn and benefit from, and they include insight into the hard lessons those individuals have learned from their venture experiences.To start, recognized by their peers for their individual efforts, the stories of Tom Maness and Lyle Leo, who are involved with community development projects that are reaping benefits for community members in terms of employment and opportunity, are highlighted.Next, two organizations -Long Plain First Nation and Khowutzun Development Corporation -are applauded for their efforts.Their stories reflect the importance to have people who understand what is needed to move forward and to have leaders who are able to help them accomplish the dreams of their communities.These communities have succeeded in that journey.Their stories give readers insights into their particular experiences.But don't stop there.These stories ring true at differing levels in many communities across this country.That is their power.Those are the gifts that they are willing to share.Capacity building starts with people and it is being done in many communities.This section ends by sharing the experiences of the people at the Ma Mawi Wi Chi Itata Centre in the City of Winnipeg.There you find an evolving story from great need to great perception.Questions such as "How can community be nurtured in a large city?" and "How can families be helped to thrive?" are highlighted.They have learned, over many years, what it takes to make a difference, and that is shared in Brendan Reimer's article.One 1 Lessons from Experience quote is especially thought-provoking, "You can't build the capacity well without actually being in the communities and building the relationships."We acknowledge those who work in the communities, build capacity, and develop and maintain relationships and partnerships.They are making a huge difference, and we are learning from their work, and celebrate their success.
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 | 0.001 | 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".