MétaCan
Menu
Back to cohort

The Language of Essence and Inference in Mental Health: Natural Law-Legal Positivism-Cognitive Dissonance

2018· article· en· W3000376032 on OpenAlexaff
Joseph Richard Crant

Bibliographic record

VenueInternational Journal of e-Healthcare Information Systems · 2018
Typearticle
Languageen
FieldNeuroscience
TopicFree Will and Agency
Canadian institutionsCanadian Psychological Association
Fundersnot available
KeywordsCognitive dissonanceLegal positivismInferencePositivismCognitionPsychologyMental healthNatural (archaeology)LawEpistemologyPhilosophySocial psychologyPolitical scienceLegal professionPsychiatryLegal realismHistory

Abstract

fetched live from OpenAlex

K-12 Teachers are experiencing more stress than ever before, and the problem isn't expected to be going away anytime soon. In this article, I want to show how it is possible to re-stimulate damaged neural pathways using insights drawn from a newly developed cognitive model, and, to show anecdotal evidence from an ongoing longitudinal qualitative study that began in 2010. The original study was started to seek support and to validate the hypothesis that; cognition is equal to an emotional response to absurd notions, thoughts, idea, etc., and that; where the individual (mind) achieves resolve, the physical brain would rest, and the body would enter relaxation phase or the; "relaxation response" [1], equal to post fight or flight, thus allowing toxins to freely flush from the body through the urine as vital organs would become unstressed, and that resolve may be inherently human. It is in the study of Dementia and Alzheimer's disease which shows us that Neural pathways in the brain can become damaged, and/or lay dormant, and it is in this research and study that we believe that we have discovered a novel approach to assist in the problem of K-12 Teacher stress and mental health, by re stimulating dormant neural pathways which inherently make it possible to exist within this modern day environment without succumbing to the adverse effects of stress 2.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.762
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.327
Teacher spread0.312 · 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 teacher head, 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of e-Healthcare Information SystemsSame topicFree Will and AgencyFrench-language works237,207