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Record W1717923299

Do Machines Have Rights? Ethics in the Age of Artificial Intelligence

2014· book-chapter· en· W1717923299 on OpenAlexaff
Paul Kellogg

Bibliographic record

VenueAurora eBooks · 2014
Typebook-chapter
Languageen
FieldSocial Sciences
TopicEthics and Social Impacts of AI
Canadian institutionsAthabasca University
Fundersnot available
KeywordsAgency (philosophy)Cognitive reframingExcellenceSociologyMedia studiesMedia ethicsPolitical scienceLibrary scienceNexus (standard)JournalismSocial scienceEngineeringLawComputer sciencePsychology
DOInot available

Abstract

fetched live from OpenAlex

Dr. David Gunkel Currently holds the position of Presidential Teaching Professor in the Department of Communication at Northern Illinois University, where he develops and teaches courses in web design and programming, information and communication technology (ICT), and cyberculture. His research and publications examine the philosophical assumptions and ethical consequences of ICT. He has published four books. He lectures and delivers award-winning papers throughout North America and Europe and he serves as the managing editor of the International Journal of Žižek Studies. His teaching has been recognized with numerous awards, including NIU's Excellence in Undergraduate Teaching Award (EUTA) in 2006 and the Presidential Teaching Professorship in 2009. David J. Gunkel was the keynote speaker for “Identity, Agency, and the Digital Nexus”, April 2013, an international symposium hosted by Athabasca University. His talk challenged the audience to reframe and rethink the “human-machine” binary in 21st century understandings of ethics and agency.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.030
Scholarly communication0.0070.012
Open science0.0010.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.160
GPT teacher head0.399
Teacher spread0.238 · 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 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

Citations2
Published2014
Admission routes1
Has abstractyes

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