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Record W2916932281 · doi:10.5334/kula.47

Networking Social Scholarship…Again

2019· article· en· W2916932281 on OpenAlexvenueno aff
Shawn Martin

Bibliographic record

VenueKULA knowledge creation dissemination and preservation studies · 2019
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipSociologyContext (archaeology)Professional associationScience communicationEngineering ethicsSocial scienceEpistemologyPublic relationsPolitical scienceHistoryLawScience educationEngineeringPedagogy

Abstract

fetched live from OpenAlex

This paper proposes to answer several questions that arise from the actions of American scientists between 1840 and 1890. How did the broader organization of science in the late nineteenth century create a system of professional disciplines? Why did the American Association for the Advancement of Science (AAAS) form, and why did specialized societies like the American Chemical Society (ACS) later found an organization separate from the AAAS? Why did these professional societies create journals, and how did these journals help to communicate science? This paper combines both quantitative textual analysis and qualitative historical and sociological methods within the context of nineteenth-century American science. It is hoped that by broadening the methods used, and by better understanding the early deliberations of scientists before there was a formal scholarly communication system, it may be possible to contextualize current debates about the need for changes in the scholarly communication system.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.998
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0040.005
Scholarly communication0.0100.009
Open science0.0010.005
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0480.009

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 head0.625
GPT teacher head0.632
Teacher spread0.007 · 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.

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

Citations0
Published2019
Admission routes1
Has abstractyes

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