Human Clarity Institute – Focus & Distraction Survey 2025 (Public Dataset v1.0)
Bibliographic record
Abstract
<b>De-identified open dataset</b> from the Human Clarity Institute’s <i>2025 Focus & Distraction Survey</i> (N = 790), conducted across the United Kingdom, United States, Canada, Australia, Ireland, and New Zealand.Measures digital attention, productivity, focus, and wellbeing.<b>This dataset underpins the following HCI reports:</b>Values vs Noise – Full ReportWhy Can’t I Focus? – Full Report<b>Dataset page:</b> https://humanclarityinstitute.com/datasets/focus-distraction-2025/<br><b>GitHub repository:</b> https://github.com/humanclarityinstitute/HCI-FocusSurvey-2025<br><b>License:</b> Creative Commons Attribution 4.0 (CC BY 4.0)🧾 Data Integrity Note (HCI 2025)This dataset forms part of the Human Clarity Institute’s post-audit verification system linking all GitHub, Zenodo, and Figshare repositories. It has been cross-checked for file, metadata, and DOI alignment and validated within its theme cluster (e.g. focus, trust, fatigue, or purpose) using pivot-table consistency tests from the <i>Digital Life 2025</i> and <i>Focus & Distraction 2025</i> frameworks.Updates are version-controlled on GitHub and mirrored across all repositories within 24 hours to maintain transparent data lineage.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.017 | 0.016 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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; both teacher heads agree on what is shown here.
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".