Bibliographic record
Abstract
Inuit hunters of the Igloolik region orient themselves on the land by understanding wind behaviour, snowdrift patterns, animal behaviour, tidal cycles, currents, and astronomical phenomena. Inuit wayfinding methods are burdensome to learn, requiring years of quiet tutoring and experience, but are perfectly reliable. Concern arose in the mid1990s that younger, less experienced hunters were beginning to rely too heavily on mechanized conveyances and electronic navigational aids. The use of global positioning system (GPS) units, particularly, has been steadily growing in Igloolik. This paper discusses the changes wrought by GPS use against the backdrop of interacting social and technological change. It argues that an understanding of these changes requires a model of technology that depends on the patterns created by devices rather than the devices (or systems of devices) themselves. A paradigmatic theory of technology based on the work of the philosopher Albert Borgmann is presented, and a distinction is made between technology that is physically and socially engaging and technology that reduces engagement with experience of the land, people, and local knowledge. It is suggested that there is a risk of turning landscapes into constructed entities or commodities, which is what happens figuratively when we are too attentive to the map and not the territory.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".