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Record W1996929124 · doi:10.1177/0196859909340349

Open Sourcing Our Way to an Online Commons

2009· article· en· W1996929124 on OpenAlexaff
Kate Milberry, Steve F. Anderson

Bibliographic record

VenueJournal of Communication Inquiry · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCommonsThe InternetCitizen journalismSociologyPraxisDemocracyInternet privacyPublic relationsRealmSocial mediaPolitical scienceWorld Wide WebMedia studiesPoliticsLawComputer science

Abstract

fetched live from OpenAlex

Understanding the social dynamics shaping the internet is vital as media power takes on new dimensions in the digital realm. The internet is increasingly necessary for participation in social life yet corporations continue to shape the online architecture to suit their own narrow commercial interests. In their drive to enclose the internet, online media companies create synergistic membranes with prescribed circuits that constrain user freedoms. Taken together, these synergistic membranes form a new layer of the internet — the Google layer, which constrains and commodifies users' range of motion within a narrow, privatized slice of the world wide web. This jeopardizes the creation of a commons-based communications system with a public service orientation, something that is essential to participatory and democratic dialogue. The open architecture of the internet, characterized and supported by free and open source software (FOSS), defends the digital commons against cyber-enclosure. Social practices and values that distinguish FOSS comprise a liberatory praxis as well as an alternative vision of social organization offline that prefigures a more democratic media system, and broadly construed, a more democratic society.

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)0.008
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.998
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0100.039
Scholarly communication0.0220.030
Open science0.0020.021
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0260.004

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 head0.236
GPT teacher head0.476
Teacher spread0.239 · 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.

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

Citations42
Published2009
Admission routes1
Has abstractyes

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