Toward an inclusive semantic interoperability: the case of Cree hydrographic features
Bibliographic record
Abstract
There has been ample work in GIScience on the formalization of ontologies but a relatively neglected area is the influence of language and culture on ontologies of geography. Although this subject has been investigated for conceptual ontologies using indigenous words denoting geographic features, this article represents the first attempt to develop a logical ontology with an indigenous group. The process of developing logical ontologies is here referred to as formalization. A methodology for formalizing ontologies with indigenous peoples is presented. A conceptual (human readable) ontology and a logical (axioms specified in mathematical logic) ontology were developed using this methodology. Research was conducted with the Cree, the largest indigenous language grouping in Canada. Results show that the geospatial ontology developed from Cree geographic concepts possesses unique design considerations: no superordinate classes were found from archival sources or Cree speakers so ontologies are structurally flat; the ontology contains some unique classes of water bodies; and the ontology challenges our notions of the generalizability of ontologies within indigenous groups. Whereas these difficulties are not insurmountable to the establishment of a cross-cultural Geospatial Semantic Web, the current plans of the World Wide Web Consortium do not adequately address them. We suggest future directions toward an inclusive semantic interoperability.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".