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Record W3142482680 · doi:10.1177/1541931213601199

This Changes Everything

2016· article· en· W3142482680 on OpenAlexaff
Andrew Thatcher, Patrick Waterson, Peter A. Hancock, Matthew C. Davis, Klaus J. Zink, Antony Hilliard

Bibliographic record

VenueProceedings of the Human Factors and Ergonomics Society Annual Meeting · 2016
Typearticle
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsClimate changeUnrestCapitalismEnvironmental ethicsPolitical scienceNatural resourcePosition (finance)Public relationsPolitical economySociologyEngineering ethicsBusinessLawEcologyPoliticsEngineering

Abstract

fetched live from OpenAlex

Naomi Klein in her recent book ‘ This Changes Everything: Capitalism vs. the Climate’ (Klein, 2014) argues that climate change represents the most pressing problem facing our age. As HFE professionals we share this view and believe there is a strong collective will to address what we see as a failure to protect the natural and social environments that support us. While still acknowledging that HFE professionals cannot address these issues alone, we believe we are in a unique position to apply relevant skills and knowledge to assist in addressing the commonly identified problem areas including pollution, climate change, renewable energy, land transformation, and social unrest amongst numerous other emerging global problems. In this panel discussion we present a number of possible contributions from the macroergonomics sub-discipline that we believe well help society find solutions to the current predicament that we find ourselves in.

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)0.004
metaresearch head score (Gemma)0.007
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: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.016
Scholarly communication0.0120.015
Open science0.0010.009
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0280.007

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 head0.012
GPT teacher head0.197
Teacher spread0.185 · 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
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

Citations54
Published2016
Admission routes1
Has abstractyes

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