Infobesity: How Does Information Overload From Digital Technologies Affect Our Relationship With Jesus
Bibliographic record
Abstract
My research project is based on the NPO of how information overload from digital technologies affects our relationship with Jesus, according to Mark 12:30-31, and how followers of Jesus can redeem digital spaces. My key insights of the research led me to the conclusion digital information overload is based on the lack of moderation and regulation of digital technologies as followers of Jesus. Furthermore, my research led me to the effects of information overload to five categories Jesus speaks of in Mark 12:30-31 of our hearts (emotions), soul (identity), mind (cognitive reasoning), strength (physical), and relationships (social skills) according to the level of digital consumption per day. I call this phenomenon Infobesity. In response, this led me to design a spiritual faith-based self-assessment resource for followers of Jesus called the Infobesity Assessment. The Infobesity Assessment is designed to empower digital users to understand their digital practices, increase their digital awareness, and how to redeem digital spaces as followers of Jesus. The design of the assessment has specialized assessments for students, parents, pastors, and the general public. As the former Next Generation Director for the Pentecostal Assemblies of Canada, I was able to use an action-based research approach with the Infobesity Assessment to access various feedback loops across the nation. This led to further data and study of digital technologies and redemptive practices. In addition, the research has allowed me to confirm a book contract to resource the broader Christian community living in a digital world.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".