Why Keep a Community-Based Focus”. In: Times of Global Interactions? Paper presented at the Fi
Bibliographic record
Abstract
IT IS A GREAT PLEASURE for me to give this address at the International Congress of Arctic Social Sciences. The guiding theme of this meeting, “Connections: local and global aspects of Arctic social systems”, is clearly the inspiration for my title. I will start with a story. A few years ago I was involved in a team project in Sachs Harbour in the Canadian western Arctic, the Inuit Observations of Climate Change study. The lead agency for the project was the International Institute for Sustainable Development (IISD). My role was to provide advice regarding the conduct of community-based research, especially with regard to local and traditional knowledge. Our IISD colleague who was in charge of the project planning meeting, came up with a very “Western looking ” workshop plan, with direct questions regarding climate change, involving the filling of index cards, and the generation of hypotheses with cause-effect linear thinking. I advised against some parts of the plan, and he did make some revisions. But the workshop was still carried out along what I thought were Western, rather than Inuit, lines of thinking and doing things. Imagine my surprise when he came back with what looked to be a lot of good workshop results and evidence of enthusiastic participation (Ford 2000). I had further surprises later when I went to Sachs Harbour myself and found out that the Inuvialuit people of Sachs were quite comfortable in the “white man’s ” style of meetings. Some of them laughed at my concerns about culturally sensitive study designs and said that these were “1970s kind of concerns”. They were no longer consid-ered to be burning issues here; the first Mackenzie Delta- Beaufort Sea oil boom in the 1970s had transformed Sachs Harbour into an English-speaking community. I did not need a translator, they said, even with the elders. I should qualify a few things. The Sachs Harbour experience is certainly not shared in all parts of the Canadian North. For example, in
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".