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Record W2296547838

Does scale matter: using different lenses to understand collaborative knowledge building

2010· article· en· W2296547838 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

VenueInternational Conference of Learning Sciences · 2010
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsJohn Abbott CollegeDawson College
Fundersnot available
KeywordsAffordanceComputer scienceFocus (optics)Knowledge managementKnowledge sharingWorld Wide WebScale (ratio)Collaborative learningData scienceHuman–computer interaction
DOInot available

Abstract

fetched live from OpenAlex

Web-based environments for communicating, networking and sharing information, often referred to collectively as Web 2.0, have become ubiquitous - e.g., Wikipedia, Facebook, Flickr, or YouTube. Understanding how such technologies can promote participation, collaboration and co-construction of knowledge, and how such affordances could be used for educational purposes has become a focus of research in the Learning Science and CSCL communities (e.g., Dohn, 2009; Greenhow et al., 2009). One important mechanism is self-organization, which includes the regulation of feedback loops and the flows of information and resources within an activity system (Holland, 1996). But the study of such mechanisms calls for new ways of thinking about the unit of analysis, and the development of analytic tools that allow us to move back and forth through levels of activity systems that are designed to promote learning. Here, we propose that content analysis can focus on the flows of resources (i.e., content knowledge, scientific artifacts, epistemic beliefs) in terms of how they are established and the factors affecting whether they are taken up by members of the community.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.096
GPT teacher head0.443
Teacher spread0.347 · 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