Bibliographic record
Abstract
The primary objective of my MA thesis project is to examine the possibilities of fostering a critical sensibility amongst Toronto urban youth by use of popular education activities and then to put these critical skills to work in the form of documentary production. I was able to perform this practical aspect of my project as part of a field placement in the Spring and Summer of 2009. During this placement I was able to provide youth (aged 14-19) in the Lawrence Heights community of Toronto, Ontario, Canada (colloquially known as "Jungle") with both the critical and technical skills necessary to create a documentary about their community and the issues that exist therein. Having these videos in hand, I am now able to reflect on both the process and the theoretical grounding of my fieldwork (which is done in this paper) as well as create an interactive and virtual home for the videos created last summer and any more that, in the future, might come ·out of the model that I implemented (www.whatisjungle.org). With my primary objective in mind, the greater, long-term, goal of my project is to help youth become more engaged with their community and begin to ask questions about their, and other, so-called "at-risk communities". I do not intend on this project '· being the final say on such an objective. Rather, it is only the beginning of a larger objective to help foster a positive sense of community in neighbourhoods negatively portrayed in the media and to help these residents become more civically engaged in order to create social change.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".