Where does the time go? A perceived shortage of time in the digital age – the Data Deluge Column
Bibliographic record
Abstract
Purpose The purpose of this column is to explore the relationship between the increasing presence of computers and communication technologies during the twentieth and twenty-first centuries on the changed perception of the level of personal time available to individuals. Design/methodology/approach In recent years, there is considerable writing in research journals and the popular media around a cluster of time-related issues which is sometimes referred to “the time crunch”, “time poverty”, “time famine”, “overwork”, “time scarcity” and countless other similar phrases. Findings A predominant contemporary struggle is that we seem to lack the time. We cannot seem to do everything we should and want to do. Originality/value The issue of time poverty is likely to remain with us in both our professional and personal lives. The question “where did the time go?” has been growing in importance since the mid-twentieth century, and it appears reasonable that even if some of the other causes of time poverty such as gender and class inequality, lack of new experiences in later life and cultural tolerance of obsessive attitudes toward work were to be miraculously reversed, the growing persistence and invasiveness of technology in our lives are not likely something that will diminish.
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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".