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History of Research Administration/Management in North America

2023· book-chapter· en· W4388525094 on OpenAlexaffabout
Kris Monahan, Toni Shaklee, Deborah Zornes

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldDecision Sciences
TopicResearch, Science, and Academia
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsAdministration (probate law)LicensePublishingGovernment (linguistics)ScholarshipPolitical sciencePublic administrationLibrary scienceManagementLaw

Abstract

fetched live from OpenAlex

Abstract In North America, the profession known as ‘research management’ elsewhere across the globe, is often known as ‘research administration’ and encompasses the activities and work associated with developing, administering, accounting for and complying with sponsor requirements, guidelines, procedures, and laws relating to externally funded projects. In the United States and Canada, the expansion of respective federal government agencies and programmes was the major factor for the need and growth of the research administration profession. Initially, administrative and business staff often administered research funding, however over the decades, a fully-fledged profession has evolved with distinct specialisations. Both the United States and Canada now have maturing professions and professional societies to organise and advance research administration. This chapter outlines the chronological origins, growth, and professionalisation of research administration in North America, with a focus on the United States and Canada. Mexico has not yet evolved a formalised research administration infrastructure.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.997
Threshold uncertainty score0.945

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0100.011
Scholarly communication0.0090.003
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.455
GPT teacher head0.496
Teacher spread0.041 · 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 designQualitative
DomainIncentives
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

Citations7
Published2023
Admission routes2
Has abstractyes

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