MétaCan
Menu
Back to cohort
Record W812246750

The Steel Dog in the Canadian Arctic: A Historical Case Study of Technological Change

2005· article· en· W812246750 on OpenAlexaboutno aff
Eric Pavri

Bibliographic record

VenueUA Campus Repository (The University of Arizona) · 2005
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsSubsistence agricultureModernization theoryArcticTechnological changePostmodernismPeriod (music)The arcticTechnological revolutionEnvironmental ethicsHistory of technologyHistorySociologySocial sciencePolitical scienceArchaeologyEconomyEpistemologyEcologyAestheticsAgricultureLawEconomicsOceanography
DOInot available

Abstract

fetched live from OpenAlex

During the "Snowmobile Revolution" of the late 1960s, the snowmobile largely supplanted the dog team as the main form of transport in the Canadian Arctic. This essay draws from historical and ethnograpphic sources to investigate practical advantages and disadvantages to adoption of the new technology, and then considers whether this episode of rapid technological change resulted in "cultural loss" in Arctic communities. While it is clear that widespread adoption of the snowmobile technological complex (machines, fuel, tools, skills, knowledge) caused significant changes in life in the Far North, it also appears that the meanings and values associated with traditional subsistence hunting were generally not lost, and in some cases were reinforced during this period of technological transition. Finally, drawing on various academic traditions such as the Social Construction of Technology school, ecological models of convergent cycles, postmodern critiques of modernization and development, and the appropriate technology movement, the essay then questions simplistic notions of cultural loss by considering the common evolution of culture and technology.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.653

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.010
Science and technology studies0.0500.014
Scholarly communication0.0060.002
Open science0.0030.004
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.039
GPT teacher head0.284
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueUA Campus Repository (The University of Arizona)Same topicIndigenous Studies and EcologyFrench-language works237,207