Exploring Karen Experiences of Urban Agriculture in Ottawa: The Importance of Place-making, Agriculture and Cultural Identity
Bibliographic record
Abstract
Situated within the field of political ecology broadly, theorizing about social nature more specifically, and drawing on qualitative methods including PhotoVoice, participant observation, and semi-structured interviews, this thesis is an extended case study exploring the complex issues and processes pertaining to urban agriculture as practiced by Karen refugees in Ottawa, Ontario, Canada.The Karen -many of whom are skilled farmers -first came to Canada (from the Thai/Burmese border) under refugee status in 2006, after enduring decades of persecution and ongoing acts of ethnic cleansing.More specifically, this paper will address the following question: What socio-cultural, economic, political and ecological benefits do practices of urban agriculture foster amongst Karen refugees in Ottawa?The results describe the transformative power of people-place relationships and highlight the need for more inclusive, just and democratic land-use management policies that are cognizant of the diverse skills and (in some cases) agrarian roots of immigrant sub-populations. CHAPTER ONE: SUBSTANTIVE CONTEXT Section 1.1: IntroductionOttawa is currently home to approximately 300 resettled Karen refugees from the Thai-Burma border camps.The Karen are a distinct ethnic group, part of a larger linguistic group called the Karenni.The Karen have suffered persecution and human rights atrocities at the hands of the Burmese military government (also known as the Tatmawdaw or the Burmese Junta).The Karen people are one of the largest ethnic minority groups in Burma.The Karen have been persecuted by the Junta for decades, along with many other ethnic minority groups in Burma.The reasons for their persecution are layered.Since the early 1960's, the Junta has set policies to wipe out any ethnic opposition groups that are struggling to assert their own identities and cultures.The Karen people have suffered atrocious human rights violations and have lived in fear for decades at the hands of the Junta.Over the last two decades, there has been an increasing outflow of refugees and migrants, including men, women and children, to both neighbouring and third countries (Smith, 2002).Currently, there are nine refugee camps in Thailand on the Burmese border.The Karen comprise the majority of the ethnic minorities living in the Thai camps.The United Nations High Commissioner for Refugees (UNHCR) and the Royal Thai Government commenced a large-scale resettlement of Burmese refugees in 2006.The UNHCR identified the 13,000 individuals in need of priority resettlement.Priority resettlement is reserved for those who have suffered "severe persecution, including torture, imprisonment, forced labour, the burning of villages and forced relocation in their homeland" (Citizenship and Immigration Canada [CIC], 2006).Resettlement is
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.019 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.006 |
| 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".