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Record W2095255427 · doi:10.1353/dss.2005.0084

'Life, Liberty and a Little Bit of Cash'

2005· article· en· W2095255427 on OpenAlexaboutno aff
Seán Butler

Bibliographic record

VenueDissent · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicRussia and Soviet political economy
Canadian institutionsnot available
Fundersnot available
KeywordsBit (key)CashLaw and economicsBusinessSociologyComputer securityComputer scienceFinance

Abstract

fetched live from OpenAlex

The Alaska Permanent Fund invests at least a quarter of the state's mineral revenues annually. Depending on the success of those investments, a dividend is paid to each Alaskan—one citizen, one share. The dividend peaked recently at nearly $2,000—that's $8,000 extra a year in the pockets of a family of four. Thanks in part to these payouts, Alaska has the smallest gap between the rich and poor in the United States. (Although everyone gets a dividend, the rich lose more of it to taxes than do the poor.) To speak of a basic income in an age of curtailed public expenditures, thinks McGill University professor and BI—supporter Myron J. Frankman, "seems like dreaming in Technicolor." Yet change often comes faster than we imagine. "No one reading the press or the journals of 1929," writes Carleton University professor Manfred Bienefeld, "could have imagined the arrival of the New Deal in 1933 in the United States." In the end, opposition to basic income stems more from a paucity of imagination than of means. In the referendum that gave birth to the Alaska Permanent Fund, about a third of Alaskans voted against it; if the vote were held again today, almost no one would oppose it. Whether we decide that a basic income is the right thing to do, the best thing to do, or the only thing to do, it seems likely that the freewheeling imagination that inspires Jay Hammond and Eduardo Suplicy will eventually work its way into the rest of us.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.045
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0080.017
Scholarly communication0.0090.008
Open science0.0010.005
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0450.010

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 head0.013
GPT teacher head0.303
Teacher spread0.290 · 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

Citations1
Published2005
Admission routes1
Has abstractyes

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