UCalgary Inclusive Map: Web Map, Data Typology, and Data Dictionary
Bibliographic record
Abstract
Maps have historically served as powerful tools for understanding spatial relationships and aided in communication, navigation, and informed decision-making. However, conventional mapping processes often overlook the diverse needs of marginalized groups, resulting in a digital divide and less inclusive maps that perpetuate inequalities in the real world. By redefining mapping processes to ensure that maps reflect the spatial behavioral distinctions and needs of all individuals, we can empower less-represented groups to make informed spatial decisions. Focusing on needs of women and those with reduced mobility, maps of urban public spaces play a pivotal role in conveying essential information about spatial features relevant to these groups' navigation and use of space. "UCalgary Inclusive Map" is designed based on an innovative Feminist GIS framework that bridges this socio-spatial gap by combining multidisciplinary ideas from feminism, urban design, accessibility research, and GIS functionalities. This map includes features and attributes representing micro-amenities within the University of Calgary's campus open spaces that influence campus accessibility, safety, diversity, and spatial awareness for everyone.
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.017 | 0.042 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.012 |
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".