Investing in community: community land trusts and affordable housing in Calgary
Bibliographic record
Abstract
The cost of affordable rental units in Calgary is amongst the highest in Canada, despite a rental vacancy rate that is 3 percent higher than the national average (Canada Mortgage and Housing Corporation, 2017). Nearly 1 in 5 Calgary households are struggling to pay for shelter costs and as of 2016, more than 42,000 households were spending more than 50 percent of their incomes on shelter, putting this population at a greater risk of becoming homeless due to job loss or from some other unexpected financial hardship (City of Calgary, 2017). Counter to popular belief, economically depressed communities with weak rental and housing markets such as Calgary following the 2015 collapse of the oil and gas sector can be subject to a critical lack of affordable housing. A soft housing market cannot make up for an insufficient range of affordable and non-market housing options. In other cities facing similar challenges, especially those in the United States, the formation of Community Land Trusts has proven to be a viable solution for providing both affordable rental and affordable ownership opportunities for residents who are struggling to afford the cost of housing in their area. This paper explores whether the Community Land Trust model is an appropriate tool to augment Calgary’s limited supply of affordable housing and will end with five recommendations to encourage the adoption of the Community Land Trust model in Calgary.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".