Illusions of Balance and Control in an Always-On Environment: A Case Study of BlackBerry Users
Bibliographic record
Abstract
This paper presents a qualitative case study of Canadian BlackBerry® users. It begins with a brief description of the BlackBerry, a handheld wireless mobile email device developed by Research in Motion1 . BlackBerry users find their devices to be empowering, allowing them more control over their environments. The BlackBerry does give its users a mechanism to exert control over the management of daily communication tasks, but by virtue of its always-on, always-connected nature, it also reinforces cultures that expect people to be accessible outside normal business hours. Rather than just a tool of liberation for its users, the BlackBerry can also be understood as an artifact that reflects and perpetuates organisational cultures in which individual employees have little control and influence. While this case study focuses on BlackBerry users, it is suggested that the findings are not unique to this device. BlackBerries and other mobile technologies have been envisioned by some as means of enforcing work-life boundaries, but this paper concludes that the use of always-on mobile devices can lead to situations where conflict between work and personal activities is exacerbated rather than reduced.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.042 | 0.023 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".