1 Connectivity in Canada’s Far North: Participatory Evaluation in Ontario’s Aboriginal Communities
Bibliographic record
Abstract
This paper tells the story about how video can become a vehicle for interactive policy making. The paper introduces the Fogo Island experience from over 30 years ago where films became a tool to bring community voices and aspirations together to the point that relocation policies were reversed. We then transport the reader to northern Ontario where the Fogo Process is being applied, now using digital video, as an evaluation and interactive policy-making tool in the context of a broadband connectivity project by Canadian First Nations. We explore how video testimonials are coherent with emerging evaluation approaches that place more emphasis on short-term outcomes –rather than results- and on narrative. We review major Communication for Development functions and we describe video testimonials as an example of participatory communication that enables beneficiaries and policy makers to understand their motivations and realities. From the beginning of time, technology has been a key element In the growth and development of societies. But Technology is More than jets and computers; it is the combination of knowledge, techniques and concepts; it is tools and machines, farms and factories. It is organization, processes and people. The cultural, historical and organizational context in which technology is developed and applied is the key to its success or failure. (Smillie, 1991:3)
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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 | 0.028 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.028 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".