Bibliographic record
Abstract
The hard work and dedication to community wellness and economic reconciliation during challenging times are highlighted in this issue of the Journal.For insights into stories of meeting challenges head-on, reflect on the articles in this issue.We are drawn into plans to support urban Indigenous entrepreneurs, to a young entrepreneur's vision for upholding tradition in the marketplace.Most appropriately, the topic of economic reconciliation is raised and a starting place is offered.A successful research program aimed at poverty reduction in several Indigenous communities across Canada is discussed, and it reminds us of how a nuanced approach to individual situations is critical for success and that it is an on-going process as needs change.Of course, the lessons that have emerged in the pandemic are important to understand.An important collaboration with different levels of government and Indigenous suppliers during Covid is documented to contribute to the growing body of literature in this area.It is with gratitude that we acknowledge our contributors and all those who undertake to bring this message of resilience and reconciliation to our readership, including Captus Press, the Journal Committee members, our reviewers, and Cando staff, who are critical to JAED's success.Please enjoy this issue as much as we enjoyed bringing it to you.In closing, the Journal Committee is excited to report our efforts in engaging with a broader audience.We have started offering open access option to authors whose papers have been accepted.Beginning this issue, we will include a DOI (a permanent identifier) for all articles.And soon, online databases such as EBSCO and ProQuest will include and distribute our work.By expanding ways to access the Journal, we hope more people will benefit from the inspiring work of practitioners and academics in the field of Indigenous community economic development featured in our journal.Ekosi
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 | 0.012 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.198 | 0.104 |
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".