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Record W2187682087

PARTICIPATORY VIDEO FOR POLICY DEVELOPMENT IN REMOTE ABORIGINAL COMMUNITIES

2006· article· en· W2187682087 on OpenAlexaboutno aff
George Ferreira, Al Lauzon, Ricardo Ramı́rez

Bibliographic record

VenueThe Atrium (University of Guelph) · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsnot available
Fundersnot available
KeywordsParticipatory action researchGovernment (linguistics)Context (archaeology)Citizen journalismScope (computer science)General partnershipPublic relationsBridge (graph theory)Public policyPolitical scienceBusinessEnvironmental planningGeographyEconomic growthComputer science
DOInot available

Abstract

fetched live from OpenAlex

This research is based on the Fogo Process which used film to bridge communication between a group of remote Newfoundland fishing communities and government policy makers and politicians in the late 1960s. The research expands the scope of the Fogo Process by integrating principles from participatory video, a development strategy used to build local capacity around socio-economic issues, participatory action research and advances in video technology. This thesis is an investigation of the role of participatory video as a tool to influence government policy-making. The research is set within the context of a group of five remote Aboriginal communities in northwestern Ontario, Canada. These communities, collectively known as Keewaytinook-Okimakanak (KO), were part of a federal pilot program to encourage innovative broadband infrastructure development across the country. These communities represent a rare research environment because prior to the introduction of broadband services, they were minimally serviced in terms of telecommunications, with one telephone available for all the communities needs. The research was initially made possible because of the need for program evaluation data. Video was used to gather testimonial stories in support of KO's Smart Program evaluation report. Video was chosen because it was felt by the evaluation team and KO leadership that Industry Canada, the primary funding agency, could make a more informed assessment if the data was contextualized through the provision of real life accounts and experiences with broadband. Very few Canadians have ever visited communities such as these and the impact that broadband was having on health care, education and community development required a communication mechanism beyond conventional evaluation approaches. Local leadership quickly realized the potential of video to link their needs with policy makers located thousands of kilometers away. Research continued into the development and dissemination of locally produced videos in the service of policy needs. During the course of the initial video productions, I provided training workshops in the communities thereby creating a critical mass of people who could produce their own video media and, in turn, teach others. After the collaborative production of twenty two videos, and numerous others produced independently by former trainees, the research culminated in the production of 'Turning the Corner'. This was a 17 minute video produced in cooperation with the Privy Council of Canada's Aboriginal Affairs Secretariat and KO leadership. The purpose of the video was to relay the message that bottom-up planning and funding strategies were essential to the success of broadband expansion across Canada's Northern Aboriginal communities. This message was based on the lessons and experience of the KO communities where broadband had transformed community life from telehealth applications and internet assisted education to overcoming isolation and community development. The video made real the need for local planning and initiative to be brought into the planning process for broadband infrastructure through a series of screenings to senior policy makers in the nation's capital, Ottawa.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.252
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2006
Admission routes1
Has abstractyes

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