Methodological Application of Bootstrapping for Predictive Data Analytics
Bibliographic record
Abstract
The existing research on Internet Addiction Disorder (IAD) focuses on conceptualization and operationalization of the problematic internet use - causing psychological disorders and social disorientation. The research supports conceptual advancements towards IAD occurrence but is inadequate in methodological applications, as IAD is a complex construct in multidisciplinary environment. The primary objective of this study is to deploy an inventive approach based on confidence interval overlap to determine whether two popularly researched influencers (psychological disorder and social disorientation) have any real significant difference towards inducing IAD. The analysis was done on primary data, collected through focused group and questionnaire methods. Statistical tool PASW v. 20 was used by enabling Bootstrapping Procedure on Unstandardized Coefficients of Regression Analysis. The study concludes that IAD is an outcome of investigated influencers but fails to signify any real difference in two interrelated concepts. The study is particularly useful for researchers using point estimates based on confidence interval overlap to determine significant differences.
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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 | 0.117 | 0.406 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".