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Record W4399713315 · doi:10.21606/drs.2024.627

Exploring the role of design for organizational learning in community interactions

2024· article· en· W4399713315 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 DRS · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsKnowledge managementComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

This paper explores the lessons learned from the COVID-19 pandemic regarding organizational learning between communities and public bodies by reviewing current literature and research studies. Public institutions interacted with a number of different communities, demographics and cultures during the pandemic. What used to be considered inclusive approaches to engagement fell short of reaching those communities most exposed to the health risks associated with working in a pandemic. This working paper presents research into different thematic spaces that explore organizational learning between institutions and communities, and the role design might play in stimulating or inhibiting trans-formation in these relationships. Informed by two pilot studies, this paper presents ongoing research into the concepts and theories of organizational learning in the context of institution-community engagement. The paper concludes by identifying potential foci for further exploration and highlights possible future directions for design research and organizational practice that span interdisciplinary frameworks.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.166

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.079
GPT teacher head0.257
Teacher spread0.178 · 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