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Record W2911657041 · doi:10.7202/1055439ar

Identity, Community, and Technology: Reflections on the Facebook Group Inuit Hunting Stories of the Day

2019· article· en· W2911657041 on OpenAlexaffvenueabout
Alexander Castleton

Bibliographic record

VenueÉtudes/Inuit/Studies · 2019
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsCarleton University
Fundersnot available
KeywordsAppropriationDystopiaSociologyArgument (complex analysis)Context (archaeology)Identity (music)PoliticsAlienationPerspective (graphical)Media studiesAestheticsEpistemologyGeographyPolitical scienceArchaeology

Abstract

fetched live from OpenAlex

In this article I reflect on a particular Inuit use of the social networking site Facebook: the group called Inuit Hunting Stories of the Day. I focus on two main issues. First, I discuss the logic behind current technologies as conceptualized by Albert Borgmann (e.g., 1984), who states that rather than being neutral tools, modern devices foster a particular “taking-up” with the world that leads to disengagement from community and meaningful practices. Arguing against this view, I discuss how Inuit Hunting Stories of the Day is an example of how the internet and Facebook are appropriated and provide meaningful engagement. Second, I follow anthropologist Claudio Aporta’s (2013) notion of ecology of technology and argue that the relationship between technology and Inuit has to be understood within an ecological framework that encompasses the broader context of political, economic, and social change, which are intertwined with the use, appropriation, adoption, and adaptation of technology. Drawing from the ecology of technology perspective, it is my central argument that technology and computer-mediated communication bring proximity to cultural practices, activities, and the land rather than provoking distance and alienation from reality, as commonly expressed in dystopian notions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0100.001
Scholarly communication0.0000.000
Open science0.0010.003
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.135
GPT teacher head0.439
Teacher spread0.304 · 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 teacher head, not a consensus.

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

Citations6
Published2019
Admission routes3
Has abstractyes

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