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Record W2016526734 · doi:10.1037/1093-4510.8.1.79

Writing Brains: Tracing the Psyche With the Graphical Method.

2005· article· en· W2016526734 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHistory of Psychology · 2005
Typearticle
Languageen
FieldPsychology
TopicAcademic and Historical Perspectives in Psychology
Canadian institutionsMcGill University
Fundersnot available
KeywordsPsycheContext (archaeology)PsychicElectroencephalographyPerspective (graphical)Cognitive scienceTRACE (psycholinguistics)Computer scienceConstraint (computer-aided design)EpistemologyPsychologyPsychoanalysisArtificial intelligenceNeurosciencePhilosophyHistoryEngineeringMedicine

Abstract

fetched live from OpenAlex

At the end of the 19th century, the graphic method kindled attempts to use it for investigating psychic processes. In Germany, Hans Berger took up this line of research, later to become the pioneer of electroencephalography (EEG). This trajectory of Berger's work is analyzed as an "enabling constraint" guiding him toward the EEG at a time when nobody else was pursuing this line of research and also causing serious methodological problems. In the epistemological perspective of this analysis, many of his problems extend beyond the local context of his work and point toward ambiguities surrounding the project to trace the psyche with the graphic method. From the mid-1930s, the EEG inspired ongoing attempts to decipher the specific meanings of these recordings, and large ensembles of machinery were mobilized, molding concepts of the psyche according to the results and the specifications of the graphic method.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.044
GPT teacher head0.384
Teacher spread0.340 · 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