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Record W3125535633 · doi:10.1525/hsns.2015.45.4.577

A Climate for Science Policy

2015· article· en· W3125535633 on OpenAlexaffabout
Matthew L. Wallace

Bibliographic record

VenueHistorical Studies in the Natural Sciences · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicPolar Research and Ecology
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsBureaucracyLegitimacyGovernment (linguistics)Political sciencePublic administrationPoliticsService (business)Public relationsSociologyEconomicsLawEconomy

Abstract

fetched live from OpenAlex

Led by the Meteorological Service of Canada, atmospheric research in Canada underwent a period of rapid growth after the end of the Second World War. Within this federal organization, and in response to operational challenges and staff shortages, there were significant investments in basic research and in research oriented toward external users within Canada. Specifically, new policies and programs were put in place to enable the organization to gain legitimacy within the scientific community and within the federal government. New links with stakeholders and, more importantly, the development of explicit policies to guide research were a prime focus. These formalized strategies for pursuing two parallel types of research generated some internal conflict, but also helped form a common scientific identity among personnel. There were concerted efforts to disseminate research products and reinforce links both with the scientific community and with external users of meteorological and climatological research. Borne out by quantitative data, this science policy–centered history sheds light on the development of research and research specializations in the field in Canada. Most importantly, it provides insight into the global postwar expansion of the atmospheric sciences, which is strongly tied to national contexts. Indeed, the quest for legitimacy and the close connection to government priorities is central to the history of the atmospheric sciences in the twentieth century. More broadly, this case study points to a possible new conception of government science driven by political, bureaucratic, and scientific imperatives, as a means to shed light on scientific networks and practices.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.140
GPT teacher head0.416
Teacher spread0.276 · 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
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

Citations2
Published2015
Admission routes2
Has abstractyes

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