RECALIBRATING DAILY LIFE: SYNCHRONIZING, COORDINATING, AND SCHEDULING THROUGH SMARTPHONES
Bibliographic record
Abstract
"This paper examines individual framings and experiences of temporal management via smartphone applications. It asks: to what extent and in what ways do configurations of smartphones and scheduling applications intervene in and restructure the temporality of practices and people’s experiences of time? The paper draws upon in-depth semi-structured interview material with (a) professional urban and suburban householders (N=25), (b) individuals transitioning to retirement (N=20), and (c) university students (N=25) to examine how a range of temporal expectations are being perceived, articulated, and negotiated in practice. Interviews included talking through temporal data of many kinds on personal devices. The analytic questions guiding interviews were: where do identifiable expectations about temporal synchronization, coordination, duration, reciprocity, and productivity come from? What are the relations between institutionally defined temporal expectations and subjective experiences of temporal ordering? Does data produced through daily activities alter the temporal contours of those activities? Are social actors reorienting themselves in-time, in relation to mediatized temporal expectations? In terms of findings, four modes of temporal management are identified and described in relation to demographic information. Following Sharma (2014), these are expressed here as ‘recalibrations’ – managing precariousness; synchronizing (to) the time of others; temporal self-disciplining; filling in future time - stressing the different ways in which temporal demands are perceived and experienced, leading to alternative efforts to coordinate and synchronize different elements of daily life through interconnected smartphone anchored applications."
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".