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Record W2608006680 · doi:10.31542/j.muse.208

On Waxen Wings: Challenges to the Public Sphere in the Network Age

2016· article· en· W2608006680 on OpenAlexaffvenue
Mike Francoeur

Bibliographic record

VenueMacEwan University Student eJournal · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsMacEwan University
Fundersnot available
KeywordsViewpointsPublic sphereThe InternetLimitingPublic relationsPublic spaceSociologyInternet privacyCybercultureDemocratizationSpace (punctuation)Political scienceMedia studiesDemocracyComputer scienceLawEngineeringWorld Wide WebVisual artsArt

Abstract

fetched live from OpenAlex

There is a tendency, particularly among Western pundits and technologists, to examine the Internet in almost universally positive terms; this is most evident in any discussion of the medium’s capacity for democratization. While the Internet has produced many great things for society in terms of cultural and economic production, some consideration must be given to the implications that such a revolutionary medium holds for the public sphere. By creating a communicative space that essentially grants everyone his or her own microphone, the Internet is fragmenting public discourse due to the proliferation of opinions and messages and the removal of traditional gatekeepers of information. More significantly, because of the structural qualities of the Internet, users no longer have to expose themselves to opinions and viewpoints that fall outside their own preconceived notions. This limits the robustness of the public sphere by limiting the healthy debate that can only occur when exposed to multiple viewpoints. Ultimately, the Internet is not going anywhere, so it is important to equip the public with the tools and knowledge to be able to navigate the digital space.

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)0.017
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0180.042
Scholarly communication0.0380.066
Open science0.0020.015
Research integrity0.0100.013
Insufficient payload (model declined to judge)0.0190.004

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 head0.044
GPT teacher head0.297
Teacher spread0.253 · 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 designNot applicable
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
Published2016
Admission routes2
Has abstractyes

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