The death of Geography: an analysis of spatial patterns of geographic education in Ontario
Bibliographic record
Abstract
<p>In Ontario geography teachers have discussed the decline in the popularity of geography classes for years. If that is the case, then students are losing access to a set of skills and knowledge that are fundamental to solving today’s global issues. If we can determine if and where geography programmes are failing in Ontario, we can target those areas with the limited resources at our disposal. Student enrollment data from the Ontario Ministry of Education in Canada was used to determine the percentage of students in each Ontario high school that were taking elective Geography courses. The ratio was geovisualized by provincial Forward Sortation Areas and it was determined that there is a decrease in the number of students taking these courses between the school years of 2011/2012- 2015/2016 and an increase in the number of schools offering no senior geography courses at all. There was also a generalized pattern of very low ratios in urban areas compared to rural Forward Sortation Areas. It is likely that numerous and complex factors are involved in creating these patterns (socioeconomic, political policy, racial divides, etc.). It is recommended that Geography teachers rebrand the subject of high school Geography as a science, technology, engineering, and math (STEM) oriented subject by highlighting the application of GIS in their courses. Geography would then appeal to students</p> <p>and parents as a vocational pathway. Additionally, teachers could use government STEM based funding to enhance teacher training and develop other resources to further the programme’s popularity.</p>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".