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Record W4283798846 · doi:10.25071/2563-3694.114

Non Linear Healing

2022· article· en· W4283798846 on OpenAlexvenueno aff
Shams Seif

Bibliographic record

VenueNew Sociology Journal of Critical Praxis · 2022
Typearticle
Languageen
FieldPsychology
TopicMusic Therapy and Health
Canadian institutionsnot available
Fundersnot available
KeywordssyncActive listeningComputer scienceAdaptation (eye)Reading (process)MultimediaHuman–computer interactionCommunicationPsychologyFrame (networking)TelecommunicationsLinguistics

Abstract

fetched live from OpenAlex

Non-Linear Healing is a minifilm that makes tangible and visible the reality of healing as a continual process, rather than a target that must be reached. The film features visuals from a dozen personal journals spanning the years 2014 to 2020. In the background, audio recordings of different entries overlap with varying degrees of intensity, urgency, and volume. Those varying degrees allow for the non-linearity of healing to be felt through the sense of movement created when the recordings lull to softer volumes, to make some readings more audible than others. Below, is an adaptation of this minifilm, which has been reproduced for print form. The adaption includes a short media curation text and video stills placed strategically to convey the spirit of the film. Additionally, multiple listening and speed typing sessions were used to create a transcript that could adequately capture the auditory experience. This transcript is included in sync with the video stills. Click here to watch the minifilm.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.044
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0040.004
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0440.006

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 head0.062
GPT teacher head0.432
Teacher spread0.370 · 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 designNot applicable
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 routes1
Has abstractyes

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