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Record W4403070808 · doi:10.54337/nlc.v11.8767

Communities of Practice

2018· article· en· W4403070808 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the International Conference on Networked Learning · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversité TÉLUQ
Fundersnot available
KeywordsGeography

Abstract

fetched live from OpenAlex

Over the last decades, there has been more and more interest in various modes of networked learning, knowledge creation, communities or practice (CoPs) but there is not yet a clear identification of the conditions to succeed in such initiatives. This interest for CoPs stems from the fact that organizations expect substantial gains from knowledge development and networked learning. Communities of practice are seen in many organizations as a source of networked learning, and ultimately of competitiveness and innovation. The interest for communities of practice arises from this objective of learning and innovation, but it is viewed as a specific form of learning and sharing, in principle more centred on the individuals and their exchanges than on “management” by the firm, although the firm does seem to have a role to play in fostering such initiatives. Thus, the use of communities of practice has emerged as a way to develop collective skills and organizational learning, in order to foster innovation and success for organizations. In this paper, we identify the conditions of success or failure of communities of practice as a mode of networked learning, knowledge management and knowledge sharing, as these conditions have not yet been established. We first define this new form of learning and knowledge sharing through communities of practice. We then present some of the results concerning success, or more precisely attainment of objectives, as success can be defined in various ways. We do this on the basis of 7 case studies of communities of practice implemented in firms. The empirical results are based on a questionnaire survey administered to the participants of these communities of practice, but also on qualitative interviews and regular work and exchanges with some of the animators and participants in these communities of practice. We highlight some interesting differences observed according to age and gender, as well as some limits and challenges that were observed in the learning and sharing process, which are often underestimated. We mainly highlight the factors which explain success, defined as attainment of objectives, and these are : commitment and motivation of participants for the attainment of objectives, as well as the presence of a leader, animator or steward.

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.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.223
GPT teacher head0.471
Teacher spread0.248 · 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