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Conclusion

2014· book-chapter· en· W2480584410 on OpenAlex
Juan Carlos Castro

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

VenueAdvances in social networking and online communities book series · 2014
Typebook-chapter
Languageen
FieldArts and Humanities
TopicArt Education and Development
Canadian institutionsConcordia University
Fundersnot available
KeywordsPopularityCode (set theory)Computer scienceHuman–computer interactionDigital contentInterface (matter)MultimediaPsychologyWorld Wide WebSocial psychologyProgramming language

Abstract

fetched live from OpenAlex

This concluding chapter serves as provocation for thinking about niche online communities as not solely constituted with human actors. It examines the question of how digital code participates with and subtly shapes niche online communities. Learning online is not only a matter of interaction between humans; it also participates with and shapes the code that learns and constructs the conditions in which one learns. Digital code delivers and dictates the content we see and learns from how an individual interacts with others. It is far from egalitarian with content as it amplifies patterns and popularity, which results in homogenous experiences and thinking for users. As a result, educators, designers of online learning environments, and researchers need to take into account the role that digital code plays in the interface it creates in teaching and learning online.

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), Science and technology studies
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 score0.915
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.276
Teacher spread0.242 · 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