The Internet Vocabulary Test for Children: preliminary development
Bibliographic record
Abstract
Purpose Currently, the only mechanisms to determine children's use of the Internet are interviews and questionnaires. To increase the validity of theory and research and ensure that practitioners and policy‐makers are guided by accurate information, an improved method of determining children's patterns of Internet use is required. The purpose of this study is to present the Internet Vocabulary Test for Children (IVTC) as a measure of Internet use in children. Design/methodology/approach The IVTC requires oral definition of ten terms (Internet, gamer, e‐mail, search engine, chat, online games, instant messaging, cheats, web site, browser). An elementary school in rural western Canada agreed to participate in trial administration of the IVTC. All children in first through sixth grade were invited to participate (n=149). Parents completed a consent form and a questionnaire. A total of 128 children (62 males and 66 females) were administered the IVTC. Findings Trial administration of the IVTC established the viability of determining children's use of the Internet with a test of expressive vocabulary. Originality/value Given the rate of population penetration coupled with rapidly changing technology, measuring children's Internet use presents challenges. Simple solutions such as the development of software and firmware to monitor children's online behavior may provide misinformation. That is, surveillance influences behavior and children's Internet activities often involve multiple users. The IVTC is not vulnerable to biased responding, is inexpensive, and easily administered.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".