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Record W4414827918 · doi:10.29173/psur403

Navigating the Digital Divide

2025· article· en· W4414827918 on OpenAlexaffvenue
Dalton Seney

Bibliographic record

VenuePolitical Science Undergraduate Review · 2025
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIndigenousDigital divideDystopiaGovernment (linguistics)CraftTransformative learningThe InternetBroadband

Abstract

fetched live from OpenAlex

The proliferation of broadband internet offers transformative potential but also introduces significant complexities, particularly in Indigenous communities. This paper examines the dual perspectives—utopian and dystopian—surrounding broadband adoption in these contexts. Utopian perspectives emphasize broadband as a tool for cultural revitalization, education, healthcare advancements, and Indigenous sovereignty, bridging geographic and social divides. Conversely, dystopian views caution against risks like cultural erosion, identity homogenization, and dependency on external infrastructures. This research explores the interplay between these opposing viewpoints, analyzing case studies and scholarly works to highlight both the opportunities and challenges broadband technology presents. The paper advocates for a balanced approach to policy development, emphasizing Indigenous leadership, collaboration, and respect for cultural protocols. Recommendations include fostering partnerships between Indigenous communities, the private sector, and government entities to craft inclusive policies that prioritize self-determination while addressing the digital divide. By embracing a culturally sensitive framework, broadband adoption can serve as a pathway for resilience, empowerment, and socio-economic development, ensuring that technological advancements honor and uplift Indigenous communities.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.944
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.324
Teacher spread0.307 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2025
Admission routes2
Has abstractyes

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