Program Evaluation In A Northern Aboriginal Setting: Assessing Impact and Benefit Agreements
Bibliographic record
Abstract
Over the past two decades, a number of Impact and Benefit Agreements (IBAs) have been established between mining firms and Aboriginal communities in support of some familiar projects across the Canadian North.Negotiated directly between mineral developers and Aboriginal communities with limited state interference, IBAs serve to manage impacts associated with the mine project and deliver tangible benefits to local communities.Notwithstanding their increasing use and potential significance, limited research has been undertaken to address a fundamental question -are they working?The dearth of research on IBA effectiveness is undoubtedly a function of its methodological complexity.In an effort to help overcome this challenge, this paper reports on the strategies employed to assess IBA effectiveness in two northern, Aboriginal locales.Drawing on insights from the program evaluation literature, the strengths and limitations of the field exercise are reflected upon with an aim of refining a procedure for future, more widespread use. 61This paper benefited from financial support from the Social Sciences and Humanities Research Council of Canada, and Indian and Northern Affairs Canada's Northern Scientific Training Program.Additionally, the authors gratefully acknowledge the assistance of, in Yellowknife, Phil Mercredi, Shirley Tsetta and Ginger Gibson, and, in Kugluktuk, Natalie Griller, Peter Taptuna, Janet Kadlun, and the student research assistants Angela Kuliktana, Manok Taipana, Beverley Anablak, Lisa Ayalik, and Danielle Meyok.Finally we would like to thank the many study participants from those two communities, as well as Dettah.
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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 | 0.068 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".