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Record W2583184975 · doi:10.26443/ijwpc.v4i1.122

The freedom vs. health paradox

2017· article· en· W2583184975 on OpenAlexvenueno aff
Craig Hassed

Bibliographic record

VenueInternational Journal of Whole Person Care · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsnot available
Fundersnot available
KeywordsHappinessOppressionInjusticeWorryCausationAnxietyState (computer science)PsychologySocial psychologyPolitical scienceLawPsychiatry

Abstract

fetched live from OpenAlex

We all want to be free but freedom is both an outer and an inner state. For example, we want to be free of the suffering brought about by external factors such as oppression, bullying, injustice, discrimination, deprivation and illness. We also want to be free inwardly in the sense of being free of the suffering brought on by internal factors such as worry, anxiety, fear, depression, obsession, compulsion, addiction and attachment. There may however be a paradox in our pursuit of freedom in that we tend to pursue the former without paying much attention to the latter. As a result we may find the opposite of what we seek – freedom, health, happiness – because outer freedom is actually dependent upon inner freedom.In this article we will explore how unfettered freedom is central to the causation of poor mental and physical health whereas, paradoxically, freedom, health and happiness are brought about by discipline, reason, restraint and setting limits.

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)0.005
metaresearch head score (Gemma)0.008
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.030
Scholarly communication0.0040.007
Open science0.0010.006
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0070.000

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 head0.047
GPT teacher head0.331
Teacher spread0.284 · 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
Published2017
Admission routes1
Has abstractyes

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