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Record W4234936942 · doi:10.29085/9781783302437.012

Increasing social connection through a community-of-practice-inspireddesign

2018· book-chapter· en· W4234936942 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

VenueInformation Systems · 2018
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Process Modeling and Analysis
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCognitionProtocol analysisWork (physics)AbstractionCoding (social sciences)PsychologyComputer scienceCognitive scienceSocial psychologyEngineeringSociologyEpistemologySocial science

Abstract

fetched live from OpenAlex

COMMENTARY: CHRISTINE URQUHARTCatherine Burns and Adam Euerby used cognitive work analysis to help design a website intended to foster community of practice principles in order to improve networking. First, we need to appreciate the history of cognitive work analysis, and where the ideas about the work domain analysis come from. It's important to recognise that both cognitive work analysis and communities of practice have evidence behind their concepts, and that they are not merely theoretical frameworks that seem to work.Cognitive work analysis comes from studies conducted by Rasmussen and colleagues at the Riso National Laboratory in Denmark in the early 2000s (Naikar, 2017). They were tasked with improving the safety of nuclear power plants in Denmark. Observations confirmed that the hardware was indeed reliable, but that, despite this, accidents could still happen. Human error appeared responsible, when workers were confronted with unfamiliar circumstances. However, the research indicated that had the workers known fully the state of the system, they could have formulated an appropriate response. Later research examined six professional technicians, problem-solving with different types of instruments, which each had a particular fault. Detailed analysis of the verbal protocols (think-aloud protocols) produced a coding scheme that revealed patterns in the reasoning used by the technicians. The technicians reasoned at different levels of abstraction (from the physical properties to the general functional purpose) and at different levels of decomposition (whole system through to a component). This formed what they termed the two-dimensional abstraction-decomposition space. Generally, the technicians started in the most abstract (purpose)/whole system corner and worked through to the opposite corner (physical form/component) – although the line of working could zig zag a little. These findings led to the first stage of cognitive work analysis modelling – the work domain analysis. This was developed by Vicente (2002) (among others) for design of interfaces that displayed three modes of cognitive reasoning: skill-based, rules-based and knowledge-based behaviour. The aim of systems designed through CWA is often to support workers in dealing with unexpected situations. Workers should be able to explore a number of ways of dealing with the situation while remaining within the boundaries of acceptable performance (Naikar, 2017).

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.984
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.007
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.053
GPT teacher head0.265
Teacher spread0.212 · 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