The Effects Resulting from Using WhatsApp in the Routines of Education Workers
Bibliographic record
Abstract
This article aims to explore the impacts of power dynamics arising from the use of the WhatsApp instant messaging application on the work routines of civil servants within a public educational institution. Utilizing the Foucauldian genealogy of power as a theoretical framework, we endeavor to conduct a critical historical analysis of the mechanics behind socially constituted power relations. Employing a qualitative case study approach, we juxtapose the analytics of power (drawing categories from the Foucauldian genealogy) with the investigative model of technological paradoxes, focusing on ‘Control vs. Chaos’ and ‘Autonomy vs. Addiction’, against data collected from interviews to uncover the power effects within this virtual space. Key findings include the observation that managers leverage a ‘system of differentiations’ to categorize and control subordinates through WhatsApp in a sophisticated and efficient manner. Moreover, the supposed enhancement of productivity through hyperconnectivity leads to compulsive smartphone use among employees, a phenomenon we interpret, following Foucault, as an institutionalized process of worker subjugation. Nonetheless, practices of resistance emerge, contesting these subjugation processes that affect the subject-workers. The institutional ‘battle’ for increased autonomy and healthier work routines emerges as one of the most potent forms of resistance against the overreach of power effects associated with WhatsApp use in the examined work contexts.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".