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Record W3215127817 · doi:10.1177/16094069211060925

Are We Done Yet? Reflections on the Sustainability of Knowledge Products

2021· article· en· W3215127817 on OpenAlexaff
Karen Gallant, Susan Hutchinson, Catherine White, Fenton Litwiller, Barbara Hamilton-Hinch, Robyn Moran

Bibliographic record

VenueInternational Journal of Qualitative Methods · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsUniversity of WaterlooUniversity of ManitobaHorizon Health NetworkDalhousie University
Fundersnot available
KeywordsTimelineKnowledge managementCLARITYBusinessSustainabilityProduct (mathematics)NegotiationProcess managementComputer sciencePolitical science

Abstract

fetched live from OpenAlex

While collaborative research approaches help ensure that knowledge products resulting from research will be relevant to stakeholders and increase the likelihood that they will be integrated into practice, there has been limited attention given to the supports essential to maintaining knowledge products. Focussing on one research project whose knowledge products are heavily used, in this paper, we discuss the challenges associated with maintaining the integrity of these knowledge products, particularly tensions associated with: (1) lack of alignment of our needs, timelines and resources as researchers with those of community partners; (2) the ongoing need to support the evolution of knowledge products despite the conclusion of funding and project infrastructure and (3) lack of clarity about decision-making responsibility related to the ongoing evolution of these knowledge products. Out of these challenges, we offer recommendations for negotiating the evolution of knowledge products and sustaining the Knowledge to Action (KTA) cycle. These recommendations focus on documenting responsibilities for knowledge product maintenance and communication, assigning expiry dates to knowledge products, identifying secure, long-term repositories for knowledge products and planning for engagement of research partners with lived experience in the maintenance of research products.

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)0.018
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.802
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.660
GPT teacher head0.685
Teacher spread0.025 · 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.

Study designQualitative
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

Citations1
Published2021
Admission routes1
Has abstractyes

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