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Record W4392353114 · doi:10.24908/ss.v22i1.16847

Review of Doctorow’s The Internet Con: How to Seize the Means of Computation

2024· article· en· W4392353114 on OpenAlexaff
Tim Ribaric

Bibliographic record

VenueSurveillance & Society · 2024
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsBrock University
Fundersnot available
KeywordsThe InternetComputationArtMedia studiesSociologyPolitical scienceArt historyComputer scienceWorld Wide WebAlgorithm

Abstract

fetched live from OpenAlex

The one thing you can always count on when Cory Doctorow publishes a book is that it is written for the widest audience you can imagine.The themes and topics he explores in his non-fiction writing primarily deal with our lives as digital citizens but are always communicated in a very accessible way.This is the primary benefit of this book, The Internet Con: How to Seize the Means of Computation (2023).No matter your understanding of the internet landscape, be it neophyte or decades old veteran, what is at stake is clearly laid out.

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)0.002
metaresearch head score (Gemma)0.010
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.004
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.005

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 head0.025
GPT teacher head0.331
Teacher spread0.306 · 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
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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