Influencing government decision makers through facilitative communication via community-produced videos: the case of remote aboriginal communities in Northwestern Ontario, Canada.
Bibliographic record
Abstract
In Canada’s diverse, vast landscape approximately one fifth of the population lives in small rural/remote communities. The capacity of these communities to influence policy and program development that directly impacts them has historically been minimal because policy is often influenced by an urban bias and a lack of sensitivity to their needs and uniqueness. Drawing on the literature on development communications, with a special emphasis on the Fogo Process as a historical antecedent, this study examines how community-produced videos influenced decision makers with regard to information and communication technology policy and programs, and the impact they have had on Aboriginal communities in Northwestern Ontario. Semistructured interviews were conducted of 22 decision makers who had seen the videos. Decision makers reported that (1) communityproduced videos provide a highly valuable context for policy makers about communities; (2) videos can be used to inform and galvanize federal staff working in the service of these communities who might not otherwise have the opportunity to visit these communities or meet their inhabitants; (3) community-produced videos are a legitimate and effective way of providing qualitative data for policy-making processes; (4) videos can serve as an organizing structure or event around which senior bureaucrats and politicians can form policy directives and influence other policy makers; and (5) videos have the potential to influence policy makers, thereby shifting the direction of policy in response to community needs and aspirations. Key words: telecommunication, remote communities, Aboriginal communities, participatory video, policy communication
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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 | 0.007 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".