From Marketing to Master Plan: An Environmental Sustainability Analysis of Toronto’s East Harbour EcoDistrict
Bibliographic record
Abstract
Due to climate change and rampant urbanization in developing countries, increased attention needs to be paid to environmental sustainability concerns, helping to shape cities for the future. Instead of offering a “blueprint”, the EcoDistrict framework for sustainability recognizes that districts, neighbourhoods, and communities experience a range of differing circumstances and priorities, allowing for flexibility through the application of context specific indicators. East Harbour, a redevelopment east of Toronto’s downtown core, aims to apply this framework. This report seeks to explore the topic of EcoDistricts, determine the current environmentally sustainable programs and tools being used by existing EcoDistricts, and to recommend next steps that Toronto would need to consider when addressing the environmental sustainability of East Harbour. \n \nThis research explores in detail the programs and tools that current EcoDistricts are using to be environmentally sustainable. In doing so, a qualitative, mixed methods research approach was used. The research methods used include a literature and documents review to provide background on and context for researching the EcoDistrict approach, and a multi-case study design to examine how EcoDistricts have successfully implemented environmental sustainability programs and tools. The case study portion included an analysis of the following EcoDistricts: (1) High Falls EcoDistrict, Rochester, New York; (2) Seaholm EcoDistrict, Austin, Texas, and; (3) Lloyd EcoDistrict, Portland, Oregon. \n \nThe research suggests that Toronto’s East Harbour EcoDistrict takes caution in terms of its marketing as it does not effectively differentiate between a vague idealism of the EcoDistrict model and the creation of an effective and applicable approach to environmental sustainability at the scale of a neighbourhood. This research has proposed three key considerations to minimize the issue of marketing and has presented ideas of how EcoDistricts can go beyond the idea of marketing sustainability that will hopefully spark a conversation that is necessary to determine how these next steps could benefit Toronto’s East Harbour EcoDistrict. The key considerations outlined by this research are: (1) the application of a comprehensive plan and roadmap; (2) the development of context specific indicators, and (3) the use of indicator monitoring and reporting.
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.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".