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Record W3006708750 · doi:10.47989/kpdc67

The work of university research administrators: Praxis and professionalization

2019· article· en· W3006708750 on OpenAlexafffundabout
Sandra Acker, Michelle K. McGinn, Caitlin Campisi

Bibliographic record

VenueJournal of Praxis in Higher Education · 2019
Typearticle
Languageen
FieldHealth Professions
TopicDoctoral Education Challenges and Solutions
Canadian institutionsBrock UniversityUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsProfessionalizationPraxisPerformativitySensemakingPublic relationsSociologyIdentity (music)Political scienceWork (physics)PedagogySocial science

Abstract

fetched live from OpenAlex

As part of a project on the social production of social science research, 19 research administrators (RAs) in five Canadian universities were interviewed about work, careers, and professionalization. While rarely featured in the higher education literature, RAs have become an important source of assistance to academics, who are increasingly expected to obtain and manage external research funding. RAs perform multiple roles, notably assisting with the complexities of grant-hunting as well as managing ethical clearance, knowledge mobilization, and related activities. Aspects normally associated with professionalization include organizations that control entry, higher degrees in the field, and clear career paths, all of which are somewhat compromised in the case of RAs. Nevertheless, most of the participants regard research administration as a profession, and we argue that it is more important to focus on the sensemaking and identity formation of these mostly female staff than to apply abstract criteria. Although their efforts do little to challenge a culture of performativity in the academy, and indeed may be regarded as supporting it, the RAs have defined for themselves a praxis dedicated to easing the burdens of the academics, helping one another, and contributing to the greater good of the university and the research enterprise.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.537

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.278
GPT teacher head0.565
Teacher spread0.287 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations16
Published2019
Admission routes3
Has abstractyes

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