MétaCan
Menu
Back to cohort

The PsychoEducation Tensor Model of Cognitive Architecture

2022· preprint· en· W4310211613 on OpenAlexaffabout
Joseph Zalman Sheppard

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldComputer Science
TopicComputational Physics and Python Applications
Canadian institutionsCamosun College
Fundersnot available
KeywordsPsychoeducationTensor (intrinsic definition)CognitionMetric (unit)PsychologyObservableArchitectureMetric tensorCognitive psychologyComputer scienceCognitive sciencePhysicsMathematicsPure mathematicsEngineeringGeographyQuantum mechanicsGeometryNeuroscience

Abstract

fetched live from OpenAlex

The PsychoEducation Tensor Model of Cognitive Architecture describes all major cognitive domains or elemenary modules (EM) in a unified symmetry isometric with Einstein's Metric Tensor. This model of cognition is likewise positioned as one of seven primary tensors that, along with the Metric Tensor, describe all observable phenomena. This model of cognition was developed soley by Joseph Zalman Sheppard in his research work at the University of Victoria and Camosun College, British Columbia, Canada.

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)0.001
metaresearch head score (Gemma)0.002
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: Other · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.005
Scholarly communication0.0030.006
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.032
GPT teacher head0.315
Teacher spread0.283 · 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
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

Citations0
Published2022
Admission routes2
Has abstractyes

Explore more

Same topicComputational Physics and Python ApplicationsFrench-language works237,207