MétaCan
Menu
Back to cohort
Record W3199078427 · doi:10.7146/irtp.v1i2.127765

Changing self in the digital age

2021· article· en· W3199078427 on OpenAlexaff
Randal G. Tonks

Bibliographic record

VenueInternational Review of Theoretical Psychologies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsCamosun College
Fundersnot available
KeywordsAgency (philosophy)Construct (python library)SelfSociologyPower (physics)AestheticsEpistemologySocial psychologyPsychologySocial scienceComputer scienceArtPhilosophy

Abstract

fetched live from OpenAlex

This article integrates William James’ (1890) theoretical model of Self with contemporary theoretical discourse and recent research on the impact of digital technology upon the Self. An overview of James’ self-theory is presented and followed by a detailed review of contemporary publications on self in our increasingly digital world; organized around the Spiritual, Social and Material realms of James’ “Me”. This is followed by this author’s extension of James’ concept of “I” into contemporary discourse on the person in terms of authenticity, agency and power. It is shown that the “Spiritual Self” is reflected in technology as fragmented, decentred and dislocated while the “Social Self” has expanded into virtual communities; continuing to seek recognition from others, but in a magnified and accelerated fashion. A cultural shift has been identified towards one of simulation and surveillance. Transformations of the “Material Self” in terms of physical bodies, interaction with the material world, and with material others, are presently observed. This author’s conceptual and theoretical exploration has also shown a corresponding loss of control and fracturing of the status of the person through the rise of surveillance and loss of personal rights that challenges the theoretical construct and everyday experience of persons.

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)0.005
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.658

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.026
GPT teacher head0.399
Teacher spread0.373 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Review of Theoretical PsychologiesSame topicImpact of Technology on AdolescentsFrench-language works237,207