Comparing Education in Rural Pakistan and Canada
Bibliographic record
Abstract
In this presentation, based on a paper I did for Dr. Sue Ann Cairns’ English 1100 class, I will be comparing education in Pakistan, where I was born, and Canada, where I am now attending Kwantlen. Children in Pakistan face a variety of serious challenges ranging from malnutrition and poor access to education and health facilities to exploitation in the form of child labor. Children’s low status in society can leave them victim to daily violence at home and in school as well as to organized trafficking and sexual exploitation. Girls are especially affected as conservative attitudes may impede them from attending or finishing school. I remember how relieved I was to finish high school, but now I have a better appreciation of our educational opportunities here. I intend to promote awareness of the value, opportunities, and comparative material luxury we have here in our educational system. I will use some statistics, but mainly I will draw from my personal experiences attending school in Serai Naurang, Pakistan and Karak, Pakistan as a child, as well as my cousin’s experiences. I will help students to become more aware of the contrast in the wealth of Canadian schools compared to schools in places such as rural Pakistan. I would like to have the audience members sit in a semi-circle so that they can see the screen easily when I show pictures and a YouTube clip that illustrates the sharp contrast between our educational opportunities and the opportunities of other students in less wealthy countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".