Bibliographic record
Abstract
In 2060, the collapse of the oil industry in Alberta has turned Calgary into a shadow of its former self. The industry’s sharp decline led to soaring vacancy rates amongst the upper tax-bracket demographics, leaving the future of the city to be determined by those most disenfranchised by the economic fallout. The evolution of infrastructure as the primary determinant of urban space has continued globally, but post-oil Calgary has slipped behind the curve. The city’s situation demands more than costly demolitions. The inextricable forces of desire, culture, gender, class, sustenance, ritual, maintenance, technology, and economics driving Calgary and its people are far too complex to be handled by the next “innovative” solution. Previously-established infrastructures which now inhibit the connections they once enabled must be reinvented, to work with the natural forces of the landscape and forge new links within remaining communities. We understand the role of oil in sites of exploration, extraction, corporate business, and consumption - but oil also created a framework for social geography. The memory of industry is intimately connected with place-based identity, and the meanings attached to a place are shaped by the stories told about its past. Spaces that exist at the confluence of social and economic factors are just as much a part of the story of the petroleum industry and what will happen when it’s gone. This thesis creates opportunities for a diversified future through reconstructing the relationship between nature and the culture of a city shaped by submission to the global network of oil.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.017 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".