Bibliographic record
Abstract
Without seeing the shadows that I would later come to appreciate, I considered my hometown, Edmonton, in the Canadian province of Alberta to be an isolated dot on an otherwise expansive map of disparate urban centers. As I became more conscious of the threads that shaped national, regional and municipal discourses, I came to realize that mine was a city that lacked , or at the very least, had a quality of being lacklustre . Canada&s;s most populated centers – Toronto, Montreal and Vancouver – were cities that carried recognition and stature in the Canadian cultural milieu. Through their history and character, they are regarded as worthy of discussion and analysis in public discourse as their size, cultural and political influence evoked certain national and international interest. They are big and bustling, and generate an element of excitement that less prominent cities cannot quite capture or attain. In contrast, as a city relegated to the edges of national discourse, Edmonton has a way of falling short of expectation among residents and non-residents alike. The shadow that is cast upon the city has a way of becoming internalized, permeating the psyche of both the city and its inhabitants, while also revealing itself, often in derogatory terms, in larger national discourse themes.
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 | 0.000 | 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 | 0.017 | 0.012 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".