New ways of mapping: using GPS mapping software to plot place names and trails
Bibliographic record
Abstract
ABSTRACT. The combined use of a GPS receiver and mapping software proved to be a straightforward, flexible, and inexpensive way of mapping and displaying (in digital or paper format) 400 place names and 37 trails used by Inuit of Igloolik, in the Eastern Canadian Arctic. The geographic coordinates of some of the places named had been collected in a previous toponymy project. Experienced hunters suggested the names of additional places, and these coordinates were added on location, using a GPS receiver. The database of place names thus created is now available to the community at the Igloolik Research Centre. The trails (most of them traditional, well-traveled routes used in Igloolik for generations) were mainly mapped while traveling, using the track function of a portable GPS unit. Other trails were drawn by experienced hunters, either on paper maps or electronically using Fugawi mapping software. The methods employed in this project are easy to use, making them helpful to local communities involved in toponymy and other mapping projects. The geographic data obtained with this method can be exported easily into text files for use with GIS software if further manipulation and analysis of the data are required.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".