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Managing Risk

2021· book-chapter· en· W4200170227 on OpenAlexaff
Alexander Corbeil, Rafal Rohozinski

Bibliographic record

VenueOxford University Press eBooks · 2021
Typebook-chapter
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsConcordia University
Fundersnot available
KeywordsMainstreamDemocracyPoliticsLimitingThe InternetEmpowermentPolitical scienceElement (criminal law)Public relationsCohesion (chemistry)Political economyLaw and economicsBusinessSociologyLawEngineering

Abstract

fetched live from OpenAlex

Abstract The Internet and social media platforms are disrupting societies and politics on a global level. While these technologies have had immense benefit, facilitating individual empowerment and establishing the data economy, they have also helped to mainstream terrorism, hate, and anti-democratic beliefs. These forces will continue to disrupt traditional democratic politics and contribute to the breakdown of societal cohesion. Regulations that ensure the protection of fundamental rights and freedoms are now a necessary element in the establishment of a new social contract for the digital age. Stakeholders must work to reimpose gatekeeper functions that the Internet has swept away, in turn limiting the ability of dangerous marginal views to influence the mainstream. However, in doing so, these same stakeholders must ensure that their efforts do not result in an Orwellian future that leaves citizens less secure and less empowered.

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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.994
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0010.002
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 head0.022
GPT teacher head0.199
Teacher spread0.177 · 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 teacher head, not a consensus.

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

Citations1
Published2021
Admission routes1
Has abstractyes

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