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Record W3171601150 · doi:10.29173/cais1208

“We’re Still Open”: Canadian News Media’s Framing of Canadian Public Libraries’ Covid-19 Responses

2021· article· en· W3171601150 on OpenAlexaffvenueabout
Nicole Dalmer, Meridith Griffin

Bibliographic record

VenueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSI · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Administration
Canadian institutionsMcMaster University
Fundersnot available
KeywordsFraming (construction)Coronavirus disease 2019 (COVID-19)NegotiationPandemicPolitical scienceConversationPublic relations2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)SociologyNews mediaMedia studiesLibrary scienceHistorySocial scienceComputer scienceMedicineCommunicationInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

COVID-19 is persistently transforming how and where public libraries are able to engage with and support their communities. While existing research at the juncture of public library services and COVID-19 has overwhelmingly examined library-produced content, this study shifts focus to media representations of library practices during COVID-19. Using frame analysis methodology, this study analyzed 218 Canadian news articles for the ways in which news stories articulate public libraries’ roles and resources during the COVID-19 pandemic. Three frames emerged: (re)negotiating the library’s space, (re)configuring the library’s roles, and (re)constructing “others”. Conclusions explore the implications of these frames, linked to a broader conversation regarding transformations to public spaces during COVID-19.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.182
Threshold uncertainty score0.949

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.011
Science and technology studies0.0500.028
Scholarly communication0.0260.008
Open science0.0030.008
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0080.001

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 head0.098
GPT teacher head0.309
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueProceedings of the Annual Conference of CAIS / Actes du congrès annuel de l ACSISame topicLibrary Science and AdministrationFrench-language works237,207