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Record W2936175451

Let’s Not Talk About the Anthropocene

2019· article· en· W2936175451 on OpenAlexaff
Jay Foster

Bibliographic record

VenueAnalecta hermeneutica · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAnthropoceneEpistemologyIrrational numberSociologyPhilosophy of sciencePsychologyPhilosophyEnvironmental ethicsMathematics
DOInot available

Abstract

fetched live from OpenAlex

The Hungarian philosopher of science Imre Lakatos once complained that Thomas Kuhn had reduced scientific research to “mob psychology. In The Structure of Scientific Revolutions (1962), Kuhn famously suggested that scientific communities organized their inquiry around particular paradigms of successful research, like Copernicus’ heliocentrism, Lavoisier’s mass chemistry or Faraday’s field theory. Kuhn’s paradigms have a family resemblance with Lakatos’ research programs, so it’s likely not this feature of Kuhn’s account that irritated Lakatos. The irritation was Kuhn’s further suggestion that scientists changed paradigms or research for reasons that were basically arational if not entirely irrational. The community of researchers is guided less by reason and logic and more by a psychological impulse to chase after specific scientific successes or potential successes. This impulse was colourfully described as a “contagious panic.” Whatever the specific merits of Lakatos’ characterization of Kuhn, it would seem difficult to deny that, for better or worse, there is at least a little mob psychology at work in academic research. The recent stampede towards “the Anthropocene” may be an apt example.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.925
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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.001

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 head0.342
GPT teacher head0.448
Teacher spread0.106 · 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
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

Citations0
Published2019
Admission routes1
Has abstractyes

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