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Record W1892277491 · doi:10.29173/cjs21329

Erasing the Social from Social Science: The Intellectual Costs of Boundary-Work and the Canadian Institute of Health Research

2014· article· en· W1892277491 on OpenAlexafffundvenueabout
Katelin Albert

Bibliographic record

VenueThe Canadian Journal of Sociology · 2014
Typearticle
Languageen
FieldDecision Sciences
TopicInterdisciplinary Research and Collaboration
Canadian institutionsUniversity of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsBoundary-workAgency (philosophy)SociologyWork (physics)Health scienceSocial researchBoundary (topology)Social sciencePublic relationsPolitical scienceMedical educationMedicineEngineering

Abstract

fetched live from OpenAlex

In 2009, Canadian social science research funding underwent a transition. Social science health-research was shifted from the Social Science and Humanities Research Council (SSHRC) to the Canadian Institute of Health Research (CIHR), an agency previously dominated by natural and medical science. This paper examines the role of health-research funding structures in legitimizing and/or delimiting what counts as ‘good’ social science health research. Engaging Gieryn’s (1983) notion of ‘boundary-work’ and interviews with qualitative social science graduate students, it investigates how applicants developed proposals for CIHR. Findings show that despite claiming to be interdisciplinary, the practical mechanisms through which CIHR funding is distributed reinforce rigid boundaries of what counts as legitimate health research. These boundaries are reinforced by applicants who felt pressure to prioritize what they perceived was what funders wanted (accommodating natural-science research culture), resulting in erased, elided, and disguised social science theories and methods common for ‘good social science.’

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)0.048
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch, Science and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.696
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0480.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0110.028
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.217
GPT teacher head0.478
Teacher spread0.261 · 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 designTheoretical or conceptual
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

Citations9
Published2014
Admission routes4
Has abstractyes

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