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Record W7040020743

Mutually-Assured Dysfunction (Darts & Letters ep55)

2022· other· en· W7040020743 on OpenAlexaboutno aff

Bibliographic record

VenueBulletin of Miscellaneous Information (Royal Gardens Kew) · 2022
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicBotanical Studies and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsNuclear powerFeelingState (computer science)PoliticsNuclear weaponPower (physics)Climate changeNuclear technology
DOInot available

Abstract

fetched live from OpenAlex

The war in Ukraine has brought nuclear technology to the forefront. There's the threat of nuclear weapons, and the danger of nuclear power plants melting down under military fire. Yet, the nuclear industry also promises to deliver us from our dependency on fossil fuels. It's an interesting duality with nuclear: is it the end of the world, or is it salvation? Professor Jessica Hurley, author of Infrastructures of Apocalypse: American Literature and the Nuclear Complex, walks us through the history of nuclear dystopia and nuclear utopia, and how they have always been closely connected.Also: happy Earth Day, even though we are not feeling particularly optimistic about the state of our planet. The war in Ukraine has brought environmental politics front-and-centre, with countries racing to extricate themselves from Russian oil and gas. Yet, in Canada, we are seeing industry push to ramp up dirty tar sands production. How will the war change energy policy? We wonk out and get into the nitty-gritty of the state of climate policy with, Mark Winfield. -------SUPPORT THE SHOW---------We need your support. If you like what you hear, chip in. You can find us on patreon.com/dartsandletters. Patreon subscribers usually get the episode a day early, and sometimes will also receive bonus content.Don't have the money to chip in this week? Not to fear, you can help in other ways. For one: subscribe, rate, and review our podcast. It helps other people find our work.----------CONTACT US---------To stay up to date, follow us on Twitter, Instagram, and Facebook. If you'd like to write to us, email darts@citedmedia.ca.----------CREDITS----------Darts and Letters is hosted and edited by Gordon Katic. The lead producer is Jay Cockburn. Our managing producer is Marc Apollonio. Our theme song and music was created by Mike Barber, our graphic design was created by Dakota Koop, and we have marketing support from Ian Sowden.This is a production of Cited Media. This episode received support from the Social Sciences and Humanities Research Council of Canada. It is part of a series of episodes on the politics of technology and techno-utopian thinking. We had research advising from Professor Tanner Mirrlees at Ontario Tech University and Professor Imre Szeman at the University of Waterloo.Darts and Letters is produced in Toronto, which is on the traditional land of Mississaugas of the Credit, the Anishnabeg, the Chippewa, the Haudenosaunee and the Wendat Peoples.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.747
Threshold uncertainty score0.999

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.7540.007

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 head0.008
GPT teacher head0.171
Teacher spread0.163 · 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; both teacher heads agree on what is shown here.

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
Published2022
Admission routes1
Has abstractyes

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