MétaCan
Menu
Back to cohort
Record W2040961336 · doi:10.1177/1474474011410276

Still searching for the Promised Land: placing women in Bruce Springsteen’s lyrical landscapes

2011· article· en· W2040961336 on OpenAlexaff
Pamela Moss

Bibliographic record

VenueCultural Geographies · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicRhetoric and Communication Studies
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsLyricsThe ImaginarySubject (documents)Ideal (ethics)Reading (process)AestheticsLiteratureSociologyHistoryArtLawPsychoanalysisPsychologyPolitical science

Abstract

fetched live from OpenAlex

By telling stories about the unevenness of the ideal of the Promised Land, Bruce Springsteen drenches landscapes with individualized renderings that speak to a collective sense of being American and living in America.Yet what is lost in this detail is the awareness that males dominate the American imaginary, that Americans are men, and that their America is masculine. A close, critical reading of Springsteen’s lyrics via Deleuze and Guattari’s ideas of ontological positivity and becoming-woman reveals complexities embedded in his American imaginary, ones rife with iconic images that assist in figuring out how women come to be an intricate part of the story without being the subject of the tale. In reading Springsteen’s lyrical landscapes, ones crafted through the ideal of the Promised Land, I use the unexplored hook of man as subject as a positive mechanism of becoming to show how the lyrics work to place women vis-a-vis men’s journeys to the Promised Land.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.015
Scholarly communication0.0060.005
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.080
GPT teacher head0.259
Teacher spread0.179 · 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
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

Citations14
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueCultural GeographiesSame topicRhetoric and Communication StudiesFrench-language works237,207