Analysing value creation in social housing construction in remote communities – application to Nunavik (Canada)
Bibliographic record
Abstract
Purpose Remote and isolated indigenous communities in Nunavik (Canada) currently face a number of housing related challenges. This paper proposes a conceptual framework to identify the factors affecting value creation within the supply chain of social housing construction in that region. The term “social” refers to the fact that governments subsidise construction and operation of these buildings intended for low-income households. Design/methodology/approach The research used a literature review and information collected from 3 semi-structured interviews with key stakeholders to identify the desired features of improvement or solutions (e.g. prefabrication) with respect to value creation. A SWOT analysis, an influence/dependence map and a causal loop diagram were developed to represent the supply chain. Findings Local job creation and the number of buildings to build were identified as the key factors that can roughly represent value creation. Energy resources, construction time, type and amount of labour force, shipping constraints, number of replacement parts and waste disposal were identified as the main factors constraining the range of solutions to implement. Practical implications The framework can be used to support the decision-making in supply chain management and the design of solutions for remote areas such as Nunavik. Originality/value This paper is the first to analyse value creation in social building construction in remote and isolated communities such as those from Nunavik. Conceptual models achieved within the framework allowed identifying the factors that could roughly represent this value creation, as well as logical relationships that link them with other factors.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".