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Record W4380242794 · doi:10.1515/9780773588608

Archibald Lampman

2013· book· en· W4380242794 on OpenAlexaboutno aff
Eric Ball

Bibliographic record

VenueMcGill-Queen's University Press eBooks · 2013
Typebook
Languageen
FieldArts and Humanities
TopicShort Stories in Global Literature
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Treasuring the past, savouring the present, and wanting to do right by the future, Archibald Lampman was a poet keenly focused on the workings of time. He was also a thinker of mystical predisposition. His goal was not to transcend time, but to find redemptive meaning within it. Archibald Lampman: Memory, Nature, Progress explores the ways in which Lampman pursued this goal in relation to the three faces of time. Memory fascinated Lampman. He relished the “alchemy” by which the dross of past experience could be left behind and the gold preserved. Nature compelled his mind and emotions, and his clear-eyed observations of both countryside and wilderness settings gave rise to a self-evolved poetics of inclusiveness. In his celebrations of nature in all its manifestations, mild or bleak, he anticipated the work of iconic Canadian painter Tom Thomson and he forecasted the environmentalism of our own time. Progress for Lampman spelled societal rectification. By forwarding the cause of social betterment, one was part of a movement larger than oneself, and this expansion, too, was redemptive. Archibald Lampman: Memory, Nature, Progress is the first book on this foundational figure in Canadian literature to appear in over twenty-five years and the first thematically focused study. Combining close analysis with biographical context, it shows how Lampman’s oeuvre was shaped by his responses to his physical surroundings and to his social-intellectual milieu, as filtered through his stubbornly independent outlook.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.637
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.189
Teacher spread0.175 · 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
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

Citations2
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueMcGill-Queen's University Press eBooksSame topicShort Stories in Global LiteratureFrench-language works237,207