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Record W6889554999 · doi:10.25904/1912/103

Gallery of the Past: Writing Historical Fiction with 19th Century Photography in Canada and Australia

2013· other· en· W6889554999 on OpenAlexaboutno aff

Bibliographic record

VenueGriffith Research Online (Griffith University, Queensland, Australia) · 2013
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
FundersGriffith University
KeywordsPhotographyTravel writingPhotojournalismPeriod (music)Work (physics)Historical record

Abstract

fetched live from OpenAlex

This thesis, consisting of a novel and dissertation, explores the writing of historical fiction, and the use of photography as research in visualising the several settings that the characters inhabit. As the novel is set in the late 19th century, the conventions of Victorian-era photography came to the forefront of the research. The story sees two fictional brothers leave their home on Vancouver Island in Canada, each traveling alone, and each with a different weight on his heart. They find themselves in towns with very real, and very documented, histories, and this is where my research into photography began. Joseph Richard, the younger brother, finds work in the town of Yale, on the Fraser River in British Columbia during the early days of the construction of the Canadian Pacific Railway. Yale was a boomtown and major depot during railway construction, and there are many photographs from the 1880s to chronicle its buildings and denizens, its remote and wild surroundings, its place in history.

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)0.002
metaresearch head score (Gemma)0.006
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: Other · Consensus signal: Other
Teacher disagreement score0.063
Threshold uncertainty score0.455

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0270.010
Scholarly communication0.0080.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.065
GPT teacher head0.293
Teacher spread0.228 · 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
Published2013
Admission routes1
Has abstractyes

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