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Record W4310345206 · doi:10.30935/cedtech/12668

The problem of the web: Can we prioritize both participatory practices and privacy?

2022· article· en· W4310345206 on OpenAlexaff
Bonnie Stewart

Bibliographic record

VenueContemporary Educational Technology · 2022
Typearticle
Languageen
FieldComputer Science
TopicOnline Learning and Analytics
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsCitizen journalismDilemmaWeb 2.0Corporate governancePublic relationsSociologyOpen educationField (mathematics)NarrativePolitical scienceEngineering ethicsComputer scienceWorld Wide WebEngineeringPedagogyBusinessThe InternetEpistemology

Abstract

fetched live from OpenAlex

This paper is a critical case study tracing the professional history of a self-professed open educator over more than two decades. It frames the narrative of an individual as a window on the broader arc of the field, from early open learning as a means of widening participation, through the rise of the participatory web at scale, to the current datafied and extractive infrastructure of higher education. It outlines how the field of online education has changed, as the web and the social and societal forces shaping use of the web have shifted. Through these lenses of change, the case study explores the dilemma facing open and participatory education at this juncture: that the current structure of the web threatens privacy, higher education governance structures, and the spirit of open, participatory sharing. The paper explores the problem of the web as one without direct solutions but does consider ways that educators might mitigate their open practice in more critical directions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.064
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.064
Threshold uncertainty score0.340

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0640.070
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0250.127
Scholarly communication0.0370.084
Open science0.0030.026
Research integrity0.0140.016
Insufficient payload (model declined to judge)0.0070.001

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 head0.042
GPT teacher head0.317
Teacher spread0.274 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations4
Published2022
Admission routes1
Has abstractyes

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