MétaCan
Menu
Back to cohort
Record W2890256541 · doi:10.18432/ari29371

A Review through Dialogue: Ruthann Knechel Johansen’s “Listening in the Silence, Seeing in the Dark: Reconstructing Life after Brain Injury”

2018· review· en· W2890256541 on OpenAlexaffvenue
Bonnie Lynn Nish

Bibliographic record

VenueArt/Research International A Transdisciplinary Journal · 2018
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSilenceWitnessContemplationActive listeningIdentity (music)PsychologyPsychoanalysisAestheticsHistoryArtLawPsychotherapistPhilosophyPolitical scienceEpistemology

Abstract

fetched live from OpenAlex

As educators, we never know what stories our students may be carrying with them. This book holds insights and treasures for anyone who has been witness to or experienced the hard fight back from a near fatal trauma and the resulting loss of identity. As educators, researchers, and parents it is important to understand the difficult struggle of returning to life after suffering from a traumatic brain injury. This book is a beautiful and heart-wrenching testament to that struggle, and the ripple-effect through family, friends, and community when circumstance changes an individual’s life in an instant. Ruthann Knechel Johansen has opened up many spaces which allow for contemplation, examination, and ultimately a dialogue in response to her son’s car accident and subsequent coma and traumatic brain injury.

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)0.003
metaresearch head score (Gemma)0.010
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0020.002
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0040.003

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 head0.280
GPT teacher head0.511
Teacher spread0.230 · 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
GenreReview

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
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueArt/Research International A Transdisciplinary JournalSame topicTraumatic Brain Injury ResearchFrench-language works237,207