Focus and Distraction 2025 (Dataset, n = 790)
Bibliographic record
Abstract
Dataset availability: Participant-level dataset files are temporarily unavailable while Human Clarity Institute completes an independent privacy and data governance review. Metadata, methodology and citation information remain available. Researchers interested in access may contact info@humanclarityinstitute.com. Version v2.2 – Documentation & Governance Update This release includes a series of documentation, governance and public dataset standardisation improvements supporting the ongoing maintenance of the Human Clarity Institute's open research library. Changes in this release include: Strengthened public dataset de-identification procedures Updated README documentation and public release guidance Updated data dictionary reflecting the current public variable structure Standardised demographic disclosure controls where appropriate Removal of geographic and participant free-text variables from the public release New SHA-256 checksums for all released files These updates improve the consistency, documentation and governance of the public dataset while preserving its research value and maintaining compatibility with the broader HCI dataset library. This version also includes: Canonical snake_case file naming Upgraded README aligned with HCI documentation standards Human Reference Layer (HRL) and Construct Registry alignment Standardised data dictionary structure This dataset is part of the Human Clarity Institute's AI–Human Experience 2025 data series. It examines digital focus, distraction triggers, and the subjective experience of maintaining attention in digitally mediated environments, including sustained attention duration, primary sources of digital distraction, behavioural coping strategies, and perceived impacts on productivity and values alignment. The public dataset includes: validated 1–7 Likert-scale measures categorical measures of single-task focus duration and perceived focus quality behavioural indicators including distraction sources, coping strategies, and focus-maintenance habits multi-select variables stored as canonical semicolon-delimited snake_case tokens standard demographic variables prepared for public release digital life exposure (daily hours online) and AI tool use frequency Data were collected on 3 September 2025 via Prolific from adults residing in the United Kingdom, United States, Australia, Canada, New Zealand and Ireland. The publicly released dataset has been cleaned, de-identified and processed under the Human Clarity Institute's public dataset protocol, which includes: canonical snake_case variable naming validated numeric and categorical value ranges standardised multi-select variable formatting removal of direct participant identifiers removal of platform identifiers and timestamps removal of geographic variables removal of participant free-text response variables demographic disclosure controls where appropriate full alignment with the accompanying data dictionary SHA-256 checksums for all released files removal of six duplicate submissions (final n = 790) The Human Clarity Institute's public release methodology is designed to preserve the maximum possible research value while reducing participant re-identification risk through documented de-identification and disclosure control procedures. This dataset contributes to understanding how digital environments shape human focus, attention sustainability and distraction-related behaviours, supporting longitudinal research into how cognitive load, attention patterns and self-regulation evolve as AI systems and digital technologies become increasingly embedded in everyday life.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.132 | 0.084 |
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".