Bibliographic record
Abstract
The World Wide Web will be revolutionized as computers gain the ability not only to process data, but also to interpret it. This computer reasoning functionality will be enabled in large part through the widespread use of a new kind of metadata called ontologies, which are formal models of concepts which computers can use to interpret data. Ontologies give computers the ability to, among other things, infer new information from data, disambiguate similar terms, and draw inferences. The concepts which different cultures use to understand the world are not the same, and so the development of ontologies to be used throughout the World Wide Web is quite problematic. One particularly stark contrast is between the geographical concepts of Western peoples, or those descended from Europeans, and indigenous peoples. Yet there has been no attempt to develop or implement a geographical ontology with an indigenous people. This thesis represents the first attempt to do this. Research was conducted with the Cree of Quebec. A geographical ontology was developed with Cree concepts, implemented in software in three different ways, and this software was tested with Cree users. Results show that geographical ontologies developed with Cree concepts have unique design considerations. Cree users were interested in the implementation of the ontology as a feature-type catalog, though the uses of the ontology to tailor the responses of a map-based graphical user interface to user input did not improve any aspects of user experience.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".