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Record W1644197095 · doi:10.3233/wor-2012-1422

Knowledge Transfer: Making Information Work

2012· article· en· W1644197095 on OpenAlexaffabout
Lynn Shaw

Bibliographic record

VenueWork · 2012
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsWestern University
Fundersnot available
KeywordsWork (physics)Information transferKnowledge managementComputer scienceEngineeringMechanical engineeringTelecommunications

Abstract

fetched live from OpenAlex

Welcome to the first columnon KnowledgeTransfer: Making Information Work! My name is Lynn Shaw, I am an occupational scientist, researcher, an associate professor in the School of Occupational Therapy at the University of Western Ontario, and I am an editorial board member of WORK. I have developed a passion for KT through my research in chronic pain and work injuries, my involvement in leading and partnering in KT initiatives in occupational therapy as an educational influential occupational therapist in a work practice network in Ontario and in other professions such as chiropractic and audiology. This column is designed to provide readers with knowledge (evidence, theory and practical knowledge) for use in enhancing knowledge brokering and networking in work related practice, in education, in research, in the community and in the workplace. For this column, a broad definition of knowledge transfer is adopted, knowledge transfer includes the exchanging and sharing of research, policy and procedural knowledge between and among a realm of knowledge producers, knowledge brokers and end users [1,2]. For instance, people from all sectors and institutions are key stakeholders in knowledge exchange andmay include but not limited to the following researchers, health professionals, career counsellors, ergonomic specialists, vocational providers, human resource specialists, workplaces, educators, employers, workers, persons with work injuries, family members, students, insurers, health and safety professionals and regulators etc. The types of knowledge transferred and exchanged in work practice can include, research, procedural information, workplace safety information, health information, career information or other information that has the main aim of enabling workers, and their families, students, health and vocational professionals, workplace parties, educators or recruiters, and trainers to consider the relevance and value of new information to make informative decisions. Use of knowledge can where the word that was facilitate adaptation, accommodation, work style redesign, positive change in behaviours or procedures or policies to prevent injuries, promote health and productivity at work and by workplaces, and assist others to advance vocational pursuits or careers. Mediums for knowledge exchange and transfer are also broadly considered in this column and will include dialogic, group or collective approaches and interactions, as well as use of network approaches, the internet, written text, social media, artistic, photo novella or symbolic expressions, etc. This column will also promote the use of theory to inform different strategies and approaches for effective KT that are sensitive to one-on-one, group or organizational specific needs and preferences. Given that the readers of this journal are international, this column is also designed to advance knowledge transfer that underscores cultural ways of knowing and disseminating informationwithin different contexts relevant to work around the world.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.735
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.010

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 head0.159
GPT teacher head0.504
Teacher spread0.345 · 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

Citations2
Published2012
Admission routes2
Has abstractyes

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