Sustainable Land Use Planning in Ontario: Protecting Against Aggregate Extraction Operations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ontario Land Use Planners who assist the Aggregate Extraction Industry often fail to recognize that aggregate extraction is among the most noxious, toxic and destructive land uses, and a major source of land use conflict. These operations frequently leave little or no possibility for rehabilitation to an economically viable state, particularly since Section 12 of the Aggregate Resources Act (ARA) does not prohibit extraction below the water table. An ill-defined and inadequate body of land use planning knowledge among Members of the Ontario Professional Planners Institute (OPPI) perpetuates an information gap regarding the severe environmental and community impacts of aggregate extraction, undermining prospects for a resilient and sustainable quality of life for present and future generations. Failure to be aware of and fully comprehend all the potential adverse effects of aggregate extraction operations hinders a land use planner’s ability to identify sensitive land uses and activities, leaving the environment and community vulnerable to impacts such as habitat loss, water contamination, toxic fumes, dust and noise, flyrock debris, visual impairment and depreciated property values. Health, safety and well-being are often overshadowed by the self-serving financial interests of private for-profit legal entities, representing a critical failure of effective land use planning. While aggregate resources (sand, gravel and crushed stone) are essential for building and road construction, their extraction in inappropriate locations can have significant negative impacts on the environment and communities. These deleterious impacts from a land use perspective must be identified, disclosed and understood before they can be effectively addressed to ensure permanent land use compatibility.
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.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it