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Record W4404881473 · doi:10.47061/jasc.v4i2.7698

Absencing as Attentional Violence and Its Impact on Well-Being

2024· article· en· W4404881473 on OpenAlexaff
Bianca Briciu

Bibliographic record

VenueJournal of Awareness-Based Systems Change · 2024
Typearticle
Languageen
FieldPsychology
TopicPsychological Well-being and Life Satisfaction
Canadian institutionsSaint Paul University
Fundersnot available
KeywordsPsychologyCognitive psychology

Abstract

fetched live from OpenAlex

This paper analyzes the impact of absencing on well-being. It explores the source of absencing in the attentional violence created by the logic of internalized capitalism: the belief that one’s self-worth is linked to productivity, the consumerist model of well-being, and the instrumentalization of relationships. The worldview created through the internalization of capitalist values leads to a stress enhancing, alienating way of life with negative consequences for well-being. Attention is diverted away from the wholeness of self and other, from the quality of relationships, creating a social field where individuals relate to themselves, others and the world through the logic of absencing. When this logic dominates the subjective structure of the inner world it reinforces self-optimization and commodified social relations that undermine well-being. Critical awareness of the internalization of capitalism reveals that even transformative approaches for well-being can become instrumentalized by the capitalist logic. This article highlights the importance of a critical lens to understand how mindfulness, and spirituality in organizations can become dominated by a capitalist worldview. It will use presencing in Theory U as a case study of a transformative approach aiming to undermine absencing while being constantly haunted by its influence.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.004
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.051
GPT teacher head0.390
Teacher spread0.339 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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