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Record W4405831437 · doi:10.51601/ijersc.v5i6.930

Research Trends On Technology-Assisted Child Sexual Abuse: A 20 Years Bibliometric Analysis

2024· article· en· W4405831437 on OpenAlexaboutno aff
Erwin Hermawan

Bibliographic record

VenueInternational Journal of Educational Research & Social Sciences · 2024
Typearticle
Languageen
FieldComputer Science
TopicCybercrime and Law Enforcement Studies
Canadian institutionsnot available
Fundersnot available
KeywordsChild sexual abuseSexual abusePsychologyCriminologyMedicineHuman factors and ergonomicsEnvironmental healthPoison control

Abstract

fetched live from OpenAlex

Technology-Assisted Child Sexual Abuse (TA-CSA) is a critical issue. This study examines the trends of TA-CSA studies globally to understand the issues. Bibliometric analysis with VOSviewer was conducted on 840 selected journal articles published in the last 20 years from the Scopus database. Between 2002 and 2023, scientific articles on the subject were published by 89 countries, with the US, UK, and Canada being the top 3 countries with the highest publication in TA-CSA. Authors from the US and UK dominate research related to TA-CSA. Research on CSA is more related to medicine, psychology, and social science. TA-CSA research tends to increase, with the highest increase occurring in 2020. Bibliometric analysis also reveals researchers' concerns and research trends regarding TA-CSA. The consistent presence of clusters focusing on TA-CSA highlights the ongoing and persistent nature of this issue. Research in this area continues to explore various aspects, including the prevalence, risk factors, and the impact on victims. It emphasises policy evaluation and victims' access to and accept various forms of protection and assistance.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics, Scholarly communication
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.1300.206
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.000
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 head0.173
GPT teacher head0.523
Teacher spread0.350 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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