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Towards Evolving Symbiotic Cognitive Education Based on Digital Twins

2019· article· en· W3044164945 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Computing and Networks
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsComputer scienceCognitionHuman–computer interactionPsychologyNeuroscience

Abstract

fetched live from OpenAlex

In the past, professional education lasted for a lifetime. Since then, the industrial revolutions have accelerated the pace of knowledge doubling from a lifetime to months, and have altered the working environment so that professionals will have to move between many jobs in their life. Are we capable of adjusting to that pace? How can we learn all that is needed in the old educational system? The time has come to revamp the educational system at the core. The new system must be personalized to match the diversity of individual abilities and styles of learning. The new system must also be based not only on the body of knowledge (BoK), but body of experience (BoX). We envisage that the new personalized system of education being sufficiently agile and interactive so that it would become evolving in its symbiosis with humans. For that to happen, we must coexist with symbiotic autonomous cognitive systems, specifically involving digital twins. This paper addresses some aspects of this view.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.978
Threshold uncertainty score0.997

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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

Quick stats

Citations11
Published2019
Admission routes1
Has abstractyes

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