MétaCan
Menu
Back to cohort
Record W249310759 · doi:10.5206/notabene.v7i1.6593

Lachrimae, or Seaven Teares by John Dowland: Tears of Lost Innocence

2014· article· en· W249310759 on OpenAlexaffvenue
Rebecca Shaw

Bibliographic record

VenueNota bene · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicTheater, Performance, and Music History
Canadian institutionsWestern University
Fundersnot available
KeywordsLuteInnocenceArtLiteratureScholarshipClassicsHistoryPsychologyPsychoanalysis

Abstract

fetched live from OpenAlex

Perhaps the best known English lutenist of the sixteenth and seventeenth centuries, John Dowland (1563? – 1626) wrote numerous pieces for the lute, as a solo and ensemble instrument, including Lachrimae, or Seaven Teares figvred in Seaven Passionate Pavans. Written in 1604, this piece was his final exploration of the popular melody that he had previously used in the lute pavan, “Lachrimae” (1596), and the lute song, “Flow my teares” (1600). Seaven Teares, for five viols and lute, is a series of seven variations whose provocative Latin titles, like Lachrimae Gementes and Lachrimae Verae, have caused some scholars to speculate that the music symbolizes either Elizabethan melancholy or the Fall of Man. However, as seventeenth-century scholarship suggests, the Elizabethan concept of melancholy was intrinsically connected to their perception of the Fall, and a close examination of Dowland’s Seaven Teares corroborates their relationship.

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)0.000
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: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.201
Teacher spread0.186 · 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.

Study designNot applicable
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

Citations0
Published2014
Admission routes2
Has abstractyes

Explore more

Same venueNota beneSame topicTheater, Performance, and Music HistoryFrench-language works237,207