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How the Crowd Can Teach

2009· book-chapter· en· W2502330652 on OpenAlex
Jon Dron, Terry Anderson

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

VenueIGI Global eBooks · 2009
Typebook-chapter
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsAthabasca University
Fundersnot available
KeywordsAffordancePerspective (graphical)Computer scienceStrengths and weaknessesThe InternetProcess (computing)Social mediaData scienceInternet privacyHuman–computer interactionArtificial intelligencePsychologyWorld Wide WebSocial psychology

Abstract

fetched live from OpenAlex

Understanding the affordances, effectiveness and applicability of new media in multiple contexts is usually a slow and evolving process with many failed applications, false starts and blind trails. As result, effective applications are usually much slower to arise than the technology itself. The global network based on ubiquitous Internet connectivity and its uneven application in both formal education and informal learning contexts demonstrates the challenges of effective use of new media. In this chapter the authors attempt to explicate the effective use of the Net for learning and teaching by differentiating three modes of networked social organization. These are defined as the Group, the Network and the Collective. The chapter explores the consequences of this perspective, observing that each has both strengths and weaknesses in different contexts and when used for different applications.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.546
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.022
GPT teacher head0.280
Teacher spread0.258 · 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