Bibliographic record
Abstract
This case study describes the experiences of an anthropologist currently conducting GIS-based ethnographic research in the Kelabit Highlands of Sarawak, Malaysia, using the e-Bario Telecentre as a local collaborating institution, a base for the input and storage of hard and soft copies of data and reports, and as a nexus for training community members to use GIS technology. Grounded in discussion of current collaborative research trends in the fields of anthropology and geography, this paper elaborates on the challenges and benefits of using the technology, facilities, and personnel currently available at the e-Bario Telecentre. It also describes how this current project is laying the foundation for a larger project that will be owned, managed, and used by the local community. This article elaborates on the social, cultural, political, economic, and environmental context in which this project is developing, demonstrating how this research project, and the transfer of technological knowledge that is a key component of it, can be both beneficial and challenging to the Kelabit community. Finally, it offers suggestions for the improvement of e-Bario by suggesting both what e-Bario can do to better serve the needs of researchers in the Kelabit Highlands and what researchers can in turn do to assist e-Bario in meeting its goals to serve the community, visitors, and other researchers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".