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Record W4386035882 · doi:10.60087/jklst.v02.n01.p60

The Impact of Social Media, Information and Communication Technology (ICT) on Reading Habit

2023· article· en· W4386035882 on OpenAlexaff
Moniruzzaman

Bibliographic record

VenueJournal of Knowledge Learning and Science Technology ISSN 2959-6386 (online) · 2023
Typearticle
Languageen
FieldComputer Science
TopicEducational Methods and Media Use
Canadian institutionsThe Journal of Student Science and Technology
Fundersnot available
KeywordsReading (process)ExploitHabitInformation and Communications TechnologySocial mediaPublic relationsNew mediaEmerging technologiesComputer sciencePsychologyInternet privacyPolitical scienceSocial psychologyWorld Wide WebComputer securityArtificial intelligence

Abstract

fetched live from OpenAlex

This systematic review study goals to carefully examine the influence of social media and information technology on the reading habit. With the fastdevelopment of technology and the universalimpact of social media in contemporary society, concerns have arisen regarding their effects on traditional reading practices. The study synthesizes existing research to provide a complete understanding of how social media and information technology have prejudiced reading habits across various individuals. By analyzing the literature, we recognize both positive and negative impacts, shedding light on possible strategies to mitigate the encounters and exploit on the opportunities presented by these technologies. The findings highlight the need for a balanced approach that harnesses the benefits of technology while preserving and enhancing reading habits

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)0.006
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.047
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0100.009
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.370
Teacher spread0.343 · 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 designObservational
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

Citations0
Published2023
Admission routes1
Has abstractyes

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