MétaCan
Menu
Back to cohort
Record W2030062908 · doi:10.1007/s00038-006-5071-0

Structuring an Inter-sector Research Partnership: A Negotiated Zone

2006· article· de· W2030062908 on OpenAlexafffund
Jocelyne Bernier, Melanie Rock, Michel Roy, Renald Bujold, Louise Potvin

Bibliographic record

VenueSozial- und Präventivmedizin · 2006
Typearticle
Languagede
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsCanadian Foundation for Healthcare ImprovementUniversity of CalgaryUniversité de Montréal
FundersCanadian Institutes of Health ResearchPublic Health AgencyCanadian Health Services Research Foundation
KeywordsGeneral partnershipNegotiationStructuringAutonomyProcess (computing)Public relationsPoliticsPolitical scienceSociologyLawComputer science

Abstract

fetched live from OpenAlex

OBJECTIVES: To document and analyze the initial steps in building a health research partnership. To enable a greater appreciation of what these processes entail and also to provide guidance in negotiating the inevitable tensions between parties with different aims and objectives. METHODS: This case study is based on participant-observation and document analysis. It employed three general analytic strategies: developing a case description, relying on theoretical propositions and thinking about rival explanations. RESULTS: The development of a research partnership framework entails a complex negotiation process marked by tensions: one of representing the interests of the various parties; and one establishing the basis for collaboration. Some factors can facilitate these processes: acknowledging the specific interests and organizational culture of the various organizations involved; designating a mediator to develop a climate of trust; and mitigating the inequalities among partners, in a process which requires considerable efforts over a rather long period of time. CONCLUSION: The process of structuring the relations among the associated partners does not end with negotiating a partnership accord. Denying this would be tantamount to denying the political nature of a research partnership, and denying those involved any autonomy in future research projects.

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)0.015
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0090.007

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 head0.688
GPT teacher head0.685
Teacher spread0.003 · 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 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

Citations28
Published2006
Admission routes2
Has abstractyes

Explore more

Same venueSozial- und PräventivmedizinSame topicHealth Policy Implementation ScienceFrench-language works237,207