Comportements stratégiques autonomes et pressions institutionnelles : le cas du BYOD
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The Bring Your Own Device phenomenon (BYOD) represents a major trend on the job market. Many employees demand to use the devices and software of their choice: mobile phones, tablets, online data storage and data sharing sites (Dropbox, iCloud), videoconferencing systems (Facetime, Skype) among others. This flexibility can be key when choosing an employer or for the purpose of talent retention. Even when these practices are not allowed, many employees, anxious to do their job better, easily find a way around. Conversely, some employers expect their employees to use their own smartphone for some tasks, thus saving on costs. Most of the research published to date on this topic focusses on security (of organizational systems and data), risks, privacy, and in specific contexts (medical settings). Our research focusses on contexts where employees want to use their own device; it tries to answer the following question: what factors and mechanisms enable the implementation of BYOD in professional spheres? We analyse this phenomenon through the lens of institutional theory (more specifically institutional pressures) and by identifying autonomous strategic behaviours of key actors; we suggest that the interplay of institutional pressures and autonomous behaviours leads to BYOD, an emergent phenomenon, that was not planned by management, and then, in turn, possibly to emerging strategies. Our methodology is a case study.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.039 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.008 |
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 it