Learning by making: exploring possibilities for a local construction ecosystem through a makerspace in Kangiqsualujjuaq
Bibliographic record
Abstract
The current construction industry in Nunavik is largely disconnected from the northern communities where the buildings themselves are constructed. Fabrication occurs in Quebec, materials are shipped north via barge and assembly is completed by a visiting southern construction crew. Furthermore, the high cost of housing in combination with rapid population growth has resulted in an ongoing housing supply crisis. Through the expansion of opportunities for local training and innovation, there lies the potential to simultaneously address several of these issues. Spaces for learning by making are an integral aspect to this effort. Inuit are their own makers and they actively continue to exercise these skills. However, vernacular design traditions have historically been ignored for the most part by southern decision makers with an institutional view of what qualifies as accepted building knowledge. This thesis addresses the question: how can the design of a makerspace serve as a means to expand local opportunities for a more sustainable, culturally reflective building ecosystem in Kangiqsualujjuaq? Review of literature on the current building delivery system and possibilities for sustainable solutions, case studies on makerspaces in northern location and an investigation of local material culture form the primary methodology. The comprehensive design of a makerspace is presented that draws inspiration from Inuit making culture with the intent to explore alternative, locally-driven avenues in the sustainable development of Kangiqsualujjuaq’s built environment. Lastly, the conclusion reflects on ways to return this work to the community and considers the wider applicability of this makerspace concept across Nunavik.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.012 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".