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Record W3044369604 · doi:10.1177/1461444820912541

Picturing the pipeline: Mapping settler colonialism on Instagram

2020· article· en· W3044369604 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueNew Media & Society · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsUniversity of TorontoUniversity of Alberta
Fundersnot available
KeywordsAffordanceColonialismMainstreamSociologyIndigenousHegemonyCitizen journalismPoliticsPipeline (software)Media studiesAestheticsPolitical scienceHistoryEcologyArchaeologyComputer scienceLaw

Abstract

fetched live from OpenAlex

Through mainstream discourses that infuse all components of society, settler superiority is naturalized in Canada. This process occurs at the expense of Indigenous peoples who continue to be displaced from the land, which is conceptualized as a ‘resource’. Despite the seemingly static nature of settler colonialism, its hegemony is both contested and reinforced through the participatory social space of Instagram. Though it is primarily known for its aesthetic and visual communication properties, Instagram’s visuality contributes substantially to public discourse, enabling resistant and political expressions around specific issues. Using data collected from Instagram, this article maps the social life of Canada’s controversial Trans Mountain pipeline issue, as it develops under medium-specific affordances. Around the Trans Mountain pipeline issue, hashtags and imagery mutually inform one another on Instagram, connecting highly located and temporal experiences with national policies, as users performatively challenge and reinforce social relations as they exist under settler colonialism.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.818
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.114
GPT teacher head0.253
Teacher spread0.139 · 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