MétaCan
Menu
Back to cohort
Record W3157108190 · doi:10.1386/jdmp_00052_1

Municipal digital infrastructure and the COVID-19 pandemic: A case study of Calgary, Canada

2021· article· en· W3157108190 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

VenueJournal of Digital Media & Policy · 2021
Typearticle
Languageen
FieldEngineering
TopicSmart Cities and Technologies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPandemicService providerWork (physics)The InternetPublic relationsGeneral partnershipPrivate sectorBusinessService (business)PopulationCoronavirus disease 2019 (COVID-19)Public administrationPolitical scienceEconomic growthSociologyEngineeringMarketing

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has placed unprecedented demands upon digital infrastructure as large portions of the population work, socialize and attend school online. National regulators worldwide have been struggling to maintain service for all citizens as the essential place of internet access in contemporary life becomes paramount. This article narrows the policy focus from the national to the municipal level. Using the case study of Calgary, Canada, the authors outline a unique and successful private–public partnership where local internet service providers have been able to adapt to the changing demands of the COVID era, supported by forward-thinking municipal policy. The authors draw upon local data sources, municipal reports and interviews with key public and private sector officials to explore how municipalities can best position themselves to provide resilient and sustainable digital service in the face of this global pandemic.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.592
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.019
GPT teacher head0.252
Teacher spread0.233 · 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