Bibliographic record
Abstract
This chapter discusses the experience of the Geomatics and Cartographic Research Centre at Carleton University in Ottawa, which has been creating cybercartographic atlases with Indigenous communities in Canada for over 15 years. The chapter describes some of the lessons learned in this ongoing process, including the important legal and ethical issues involved, as well as the development of the innovative Nunaliit data management framework used to create the atlases. A cybercartographic atlas is quite different from a conventional atlas, and is a metaphor for all kinds of qualitative and quantitative information linked by location. These are interactive, multimedia and multisensory on-line products. They are quite different from standard geographic information system (GIS) products. The atlases are community controlled and community generated and telling stories from a community perspective is one of their central features. Both mapping and storytelling are basic human instincts. This chapter will describe some of the lessons emerging from this ongoing experience, one of which is the importance of the processes by which the atlases are produced which is equally, if not more important, than the products. Nunalliit means community in Inuktituk, and is the software framework used to produce the atlases. It has been designed so that communities can produce their own atlases rather than depend on external expertise. Community ownership and control is of great importance, and creating the atlases often requires a decentralized and distributed data management approach.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".