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Record W4396513843 · doi:10.4324/9781003282075-15

Internet use genres

2024· book-chapter· en· W4396513843 on OpenAlexaboutno aff
Maria Bakardjieva, Isabel Pavez Andonaegui, Teresa Correa, Trang Pham

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldComputer Science
TopicDigital Communication and Language
Canadian institutionsnot available
Fundersnot available
KeywordsThe InternetComputer scienceInternet privacyWorld Wide Web

Abstract

fetched live from OpenAlex

Public policies across the world have focused on providing Internet access to residents of rural areas. This chapter looks at data from previous studies in three countries at a different point in their digital inclusion process highlighting the ways rural inhabitants appropriate Internet technologies. The findings propose a conceptual tool for the analysis of the characteristics of these appropriations and their link to the specific social context in which they emerge: Internet use genres. This concept integrates ideas from the critical theory of technology, sociological phenomenology, and genre theory. By revisiting the results of independent studies conducted in rural areas of Canada, Chile, and Vietnam, we trace the emerging Internet use genres to elements of the participants’ relevance systems and socio-biographical situations that in turn are shaped by the geographical, socio-economic, and cultural conditions of their communities. The parallels between the use genres evolved by rural residents despite the distinct social and political profiles of the home societies is an indication that Internet use genres respond to recurring local situations and are highly malleable and subject to user agency. Being able to identify, classify, and anticipate their evolution provides researchers and policymakers with a nuanced understanding of Internet adoption processes in rural communities. It also opens possibilities for exploring the inclusion of end-users’ choices in technological development.

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)0.000
metaresearch head score (Gemma)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.930
Threshold uncertainty score0.995

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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.005

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 head0.060
GPT teacher head0.252
Teacher spread0.192 · 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 designTheoretical or conceptual
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

Citations0
Published2024
Admission routes1
Has abstractyes

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Same topicDigital Communication and LanguageFrench-language works237,207