The Body in Technology and Organization Studies
Bibliographic record
Abstract
This symposium explores how the field of technology and organization studies may benefit from paying more attention to the role of the body. As recent studies have shown, lived experiences, sensemaking, knowing, coordination and overall performance at work become reconfigured as emerging technologies reshape physical engagement and human interaction. Four papers will be presented exploring questions around how a focus on the body allows us to understand changes in knowing; situation awareness; movement at work; and coordination. The diversity of research settings (sports, police work, restaurants, and healthcare) offers us the possibility to reflect on boundary conditions and build conceptual bridges in examining the importance of an embodiment perspective for theorizing technology, work and organizing. Two discussants with expertise in embodiment will help connect the studies with the growing stream of literature on embodiment and will facilitate discussion with the audience to explore avenues for future research. You win some, you lose some: Worker self-knowing through digital self-tracking technologies Author: Lorna Anne Downie; Vrije U. Amsterdam Author: Ella Hafermalz; Vrije U. Amsterdam Author: Marleen Huysman; KIN Center for Digital Innovation, Vrije U. Amsterdam Author: Stella Pachidi; U. of Cambridge Police officers’ embodied and material realities of accessing information in action Author: Lauren Waardenburg; ESSEC Business School Author: Ella Hafermalz; Vrije U. Amsterdam Moving at work: An ethnography of autonomous mobile robots at a restaurant Author: Melissa Sexton; Vrije U. Amsterdam Author: Anastasia Sergeeva; Vrije U. Amsterdam Author: Maura Soekijad; Vrije U. Amsterdam The space between us: Embodiment and the configuration of coordination Author: Samer Faraj; McGill U. Author: Karla Sayegh; U. of Cambridge
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".