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Record W4225844335 · doi:10.17077/etd.006068

Gerhard Krapf, organist

2021· dissertation· en· W4225844335 on OpenAlexaboutno aff
Daniel Laaveg

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldArts and Humanities
TopicMusicology and Musical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

Gerhard Krapf (1924–2008) contributed to the North American Orgelbewegung (Organ Reform Movement) as an organist, professor, church musician, writer, consultant, and composer. Born in Germany and drafted into the German Army for World War II, he survived three years of war and three years in Soviet labor camps, before resuming his education and immigrating to the United States in 1953 for his multi-faceted career. He started the organ department at the University of Iowa in 1961 and the University of Alberta in 1977. Krapf displayed a strong reference for teaching and performing polyphonic music of the Renaissance and Baroque using historically-informed practices, though he consistently demonstrated a flexible and non-dogmatic approach. Krapf’s articles, books, and translations demonstrate his passion for the tenets of the Orgelbewegung, particularly the superiority of the mechanical-action instruments. In this Essay, seven of his organ solo compositions are studied, including Totentanz, Second Organ Sonata for Thanksgiving, and Chorale Triptych on Lord, Keep Us Steadfast in Your Word. The variation of hymn tunes forms the basis of his predominantly multi-movement works. His music features independent contrapuntal lines, transparent registrations, animated rhythms, and a harmonic language that gradually shifted from highly dissonant with little use of triads to thoroughly tonal and standard chord progressions.

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)0.002
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: Other · Consensus signal: Other
Teacher disagreement score0.021
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0210.016

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 head0.020
GPT teacher head0.226
Teacher spread0.206 · 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
Published2021
Admission routes1
Has abstractyes

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