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Spectrum Sovereignty on Tribal Lands: Assessing the Digital Reservations Act

2023· article· en· W4387969010 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Information Policy · 2023
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsnot available
FundersUniversity of TorontoUniversity of Notre DameNational Science Foundation
KeywordsSovereigntyIndigenousAutonomyCorporate governanceGovernment (linguistics)Spectrum managementInternet governancePublic administrationThe InternetPolitical scienceBusinessLawTelecommunicationsEngineeringComputer sciencePoliticsFinance

Abstract

fetched live from OpenAlex

ABSTRACT The current system for managing spectrum in the United States gives the federal government essentially all authority over electromagnetic spectrum management and governance on tribal lands. The Deploying the Internet by Guaranteeing Indian Tribes Autonomy over Licensing (DIGITAL) Reservations Act envisions a system of spectrum governance that affirms tribal self-determination in managing and licensing the natural resource called spectrum. Though the DIGITAL Reservations Act has yet to be passed into law, it outlines a set of principles that are essential to guide equitable policymaking related to Indigenous nations. We analyze the Act and discuss the opportunities and challenges offered by this framework.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.018
GPT teacher head0.293
Teacher spread0.275 · 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