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Technology Stewarding as a Medium to Develop and Sustain Niche Online Communities

2014· book-chapter· en· W2486144485 on OpenAlexaff
Ann-Louise Davidson, Issa Gulka, André Bittencourt Valle, Chantal Castonguay

Bibliographic record

VenueAdvances in social networking and online communities book series · 2014
Typebook-chapter
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsConcordia University
Fundersnot available
KeywordsOperationalizationArgument (complex analysis)LivelihoodVignetteOnline communityTheme (computing)Knowledge managementSociologyPublic relationsEngineeringPsychologyPolitical scienceComputer scienceWorld Wide WebGeographyMedicineSocial psychologyEpistemology

Abstract

fetched live from OpenAlex

The goal of this chapter is to operationalize the theoretical argument about the importance of technology stewarding in the development of niche online communities. To bring about successful changes in a system, technology stewards propose technological solutions that will help to solve the problems of a community they know well. Assuming the role of a technology steward is the theme of a course in the M.A. in Educational Technology Program at Concordia University. Students enrolled in Social Computing and Computer Supported Collaborative Learning have twelve weeks to find a community that needs technological solutions and to propose such solutions to help them develop and to sustain their livelihood. The body of this chapter presents three vignettes, each consisting of a student project. Andre and Chantal’s vignettes both describe the creation of online learning communities with second language students, while Issa’s vignette describes the creation of an online support community of people suffering from the symptoms of a systemic illness. The three vignettes describe the approach in which each project was undertaken, the outcomes, and the lessons learned.

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.

How this classification was reachedexpand

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), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.875
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.004
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 head0.043
GPT teacher head0.371
Teacher spread0.328 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2014
Admission routes1
Has abstractyes

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