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Record W6940976120 · doi:10.11575/prism/2889

Private sector engagement in affordable new rental construction in Calgary

2008· other· en· W6940976120 on OpenAlexaboutno aff

Bibliographic record

VenuePRISM (University of Calgary) · 2008
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsAffordable housingPrivate sectorRentingContext (archaeology)Government (linguistics)Production (economics)

Abstract

fetched live from OpenAlex

Within the past decade the responsibility for affordable rental housing has increasingly shifted from the federal and provincial levels of government to the municipal level. With an increase in demand for affordable housing, governments have had to consider alternative approaches to increase the production of affordable rental housing, such as engaging the private sector. The MOP examines how to best engage the private sector to increase the production of affordable rental housing in Calgary context by analysing the most effective combination of government interventions. These interventions could be in the form of regulatory, fiscal, financial or institutional measures which are analyzed based on the effect of the intervention on a private developers' bottom line, the necessary infrastructure required to implement the intervention, which would affect the likelihood of the responsible organization or government to actually implement it and the amount of production the intervention could leverage. Key informant interviews add additional perspective on barriers and opportunities for the private sector to increase production of affordable rental housing. A combination of the interviews and analysis of potential interventions within the context of the rental housing market within the City of Calgary and the private development industry provides the foundation for all recommendations.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.794
Threshold uncertainty score0.409

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.002
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.000

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.

Opus teacher head0.012
GPT teacher head0.176
Teacher spread0.164 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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