MétaCan
Menu
Back to cohort
Record W2025699753 · doi:10.3138/cras.2014.s07

Laying Tracks and Tracking Change: An Interrogation of Nostalgia, National Identity, and the Railway in Contemporary Photography

2014· article· en· W2025699753 on OpenAlexvenueno aff
Reilley Bishop-Stall

Bibliographic record

VenueCanadian Review of American Studies · 2014
Typearticle
Languageen
FieldPsychology
TopicNostalgia and Consumer Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsAcknowledgementInterrogationPhotographyIdentity (music)AestheticsSociologyHistorySpace (punctuation)PerceptionVisual artsArtArchaeologyEpistemologyPhilosophyComputer science

Abstract

fetched live from OpenAlex

Abstract: From the time of the earliest photographs commissioned to document the construction of the railway, the camera and the train have enjoyed a mutually beneficial, almost symbiotic, relationship, and despite, or perhaps owing to, the parallel decline of both traditional forms in more recent decades, the two technologies remain remarkably intertwined. This project examines the work of two contemporary artists, Scott Conarroe and Mark Ruwedel, whose photographs depict the enduring presence of the railway industry throughout North America. Exploring the coeval development of photography and the railway and the contribution of both to the construction of national identity in the nineteenth century, I interrogate the two technologies’ shared relevance to questions of transportation, communication, and perception in the present, in order to explore how a nation or community is affected by infrastructure that remains from technologies that once were vital to its creation and unification. Providing a critique of the growing literature on nostalgia, I argue that ethical engagement, both within the present and in regards to the past, depends upon an acknowledgement of the heterotemporality of space and the heterogeneity of time.

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)0.011
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.005
Science and technology studies0.0080.050
Scholarly communication0.0080.008
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.128
GPT teacher head0.408
Teacher spread0.280 · 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 designQualitative
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
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Review of American StudiesSame topicNostalgia and Consumer BehaviorFrench-language works237,207