MétaCan
Menu
Back to cohort
Record W2600397269

Affecting education through portraits of the emotional world of learning

2017· article· en· W2600397269 on OpenAlexaff
Deborah P. Britzman, Noel Arthur Davies Glover, Lucy Angus, Aziz Güzel

Bibliographic record

Venue2017 Conference of the Canadian Society for the Study of Education · 2017
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsYork University
Fundersnot available
KeywordsCuriosityCreativityPsychologyPresentation (obstetrics)PhenomenonSubject (documents)PortraitPedagogyEpistemologySocial psychologyVisual arts
DOInot available

Abstract

fetched live from OpenAlex

The symposium consists of three presentations based on close readings of case studies of the lives of writers, teachers, scientists, psychoanalysts, and youth along with a coda on the crucial topic of why and how attending to the emotional world of teaching and learning involves the psychical procedures of destruction and creativity; refusals and acceptance; withdrawal and curiosity; and love and hate. The work of the symposium is to provide an affecting model of education dedicated to understanding the emotional world; develop a case study approach to analyzing crucial paradoxes of education; extend the growing field of psychoanalysis and education to the specificities of teacher education; and finally, to provide new approaches to thinking about teacher education through the lens of human development. Each presentation conceives development through intersubjectivity and as uneven, subject to regressions and creativity, made from experiences of continuity and discontinuity, and, all told as a response to the emotional situation of being with others.  What joins the symposium is the problem of presenting the affecting qualities of the emotional world found in the question: Is the capacity to represent and understand one’s development a creative experience and itself a developmental phenomenon?

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.233
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.118
GPT teacher head0.409
Teacher spread0.291 · 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 designObservational
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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same venue2017 Conference of the Canadian Society for the Study of EducationSame topicEducational and Psychological AssessmentsFrench-language works237,207