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Hubris and Sciences

2018· preprint· en· W2787467754 on OpenAlexaff
Eleftherios P. Diamandis, Nick Bouras

Bibliographic record

VenueF1000Research · 2018
Typepreprint
Languageen
FieldSocial Sciences
TopicHistory, Medicine, and Leadership
Canadian institutionsUniversity of Toronto
FundersAbbott Diagnostics
KeywordsHubrisPrideOverconfidence effectNarcissismPsychologyHumilitySocial psychologyPower (physics)ScrutinyCrueltyEnvironmental ethicsPolitical scienceCriminologyLawHistory

Abstract

fetched live from OpenAlex

There has been an increasing awareness of the importance of leadership and decision making, including scientists and academics, over recent times. By whom and how decisions are made can have serious implications across all levels of society. Several people have been successful in their life and have been inflicted by excessive pride and self-confidence. There are times when the manifestations of such behaviours demonstrate noticeable signs of narcissism and on extreme cases, hubris. Hubris is an old concept originated from the Greek mythology. The risk of hubris affects politicians, leaders in business, scientists, academia, the military, entertainers, athletes and doctors (among many others). Power, especially absolute and unchecked power, is intoxicating and is manifested behaviourally in a variety of ways, ranging from amplified cognitive functions to lack of inhibition, poor judgment, extreme narcissism, deviant behaviour, and even cruelty. Hubristic behaviour of overconfidence, extreme pride together with an unwillingness to disregard advice makes powerful people in leadership positions to over-reach themselves with negative consequences for themselves and others. As the dangerous consequences of hubristic behaviours become more apparent and well described it is imperative that individuals, organisations and governments act to prevent such phenomena. Responsible leaders, including acclaimed scientists should exercise greater humility to the complexity and inherent uncertainty of their activities and strive to seek out and challenge hubristic behaviours.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaScience and technology studies
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptno category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
models splitAgreement compares identical category sets and study designs across arms.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.211

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.008
Scholarly communication0.0060.005
Open science0.0010.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0630.015

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 head0.299
GPT teacher head0.480
Teacher spread0.181 · 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

Labeled directly by 2 models reading the full record.

Science and technology studies

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designTheoretical or conceptual
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

Citations6
Published2018
Admission routes1
Has abstractyes

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