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Record W2607360249 · doi:10.1525/hsns.2017.47.2.164

Claiming Spaces for Science

2017· article· en· W2607360249 on OpenAlexaboutno aff
Adam M. Sowards

Bibliographic record

VenueHistorical Studies in the Natural Sciences · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsRhetorical questionNarrativePolitySpace (punctuation)SociologyArcticPoliticsThe arcticEpistemologyMedia studiesPolitical scienceLawPhilosophyLinguisticsEcology

Abstract

fetched live from OpenAlex

Exploration has always centered on claims: for country, for commerce, for character. Claims for useful scientific knowledge also grew out of exploration’s varied activities across space and time. The history of the Canadian Arctic Expedition of 1913–18 exposes the complicated process of claim-making. The expedition operated in and made claims on many spaces, both material and rhetorical, or, put differently, in several natural and discursive spaces. In making claims for science, the explorer-scientists navigated competing demands on their commitments and activities from their own predilections and from external forces. Incorporating Arctic spaces into the Canadian polity had become a high priority during the era when the CAE traversed the Arctic. Science through exploration—practices on the ground and especially through scientific and popular discourse—facilitated this integration. So, claiming space was something done on the ground, through professional literature, and within popular narratives—and not always for the same ends. The resulting narrative tensions reveal the messy material, political, and rhetorical spaces where humans do science. This article demonstrates how explorer-scientists claimed material and discursive spaces to establish and solidify their scientific authority. When the CAE claimed its spaces in nature, nation, and narrative, it refracted a reciprocal process whereby the demands of environment, state, and discourse also claimed the CAE.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.006
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.407
Teacher spread0.279 · 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; both teacher heads agree on what is shown here.

Study designObservational
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
Published2017
Admission routes1
Has abstractyes

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