Human Clarity Institute – Focus & Distraction Survey 2025 (Public Dataset v1.0)
Bibliographic record
Abstract
De-identified open dataset from the Human Clarity Institute’s 2025 Focus & Distraction Survey (N = 790), conducted across the United Kingdom, United States, Canada, Australia, Ireland, and New Zealand. Measures digital attention, productivity, focus, and wellbeing. This dataset underpins the following HCI reports: **Full reports using this dataset:**- [Values vs Noise – Full Report](https://humanclarityinstitute.com/reports/values-vs-noise-full-report/)- [Why Can’t I Focus? – Full Report](https://humanclarityinstitute.com/reports/why-cant-i-focus-full-report/) **Dataset page:** (https://humanclarityinstitute.com/datasets/focus-distraction-2025/) **License:** Creative Commons Attribution 4.0 (CC BY 4.0) Data Integrity and Verification Note:This dataset has undergone a full internal audit by the Human Clarity Institute (HCI) to confirm the accuracy and consistency of all source data and metadata. All checks were completed during HCI’s 2025 data audit to ensure long-term reliability across the Institute’s open datasets. Verification was performed manually before the introduction of HCI’s 2025 structured data-validation framework. Cross-checks included pivot-table consistency analysis, manual inspection of survey responses, and reconciliation of summary statistics with underlying data. No survey responses were altered; only metadata formatting and DOI alignment were adjusted for cross-platform consistency. This dataset is considered final and integrity-verified as part of HCI’s 2025 audit, and serves as a reference example of HCI’s early verification methodology.
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 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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.133 | 0.078 |
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".