It Takes (At Least) Two: Embracing a Relational Ontology to the Study of Work in the 21st Century
Bibliographic record
Abstract
The future of work has long been a subject of speculation, with predictions often reflecting the diverse frames of reference of those who make them. Although predictions are rarely proven to be accurate, profound changes in work practices, organizational structures, and employment arrangements are nonetheless occurring. This symposium examines the benefits of adopting a relational ontology to the study of these transformations, foregrounding how interactions among systems, people, and objects influence the evolving nature of work. Specifically, each of the presentations builds on the premise that social phenomena are not fixed and stable entities but unfolding interactions comprised of multiple actors and actions, both social and material. The four featured papers provide empirical depth, examining diverse contexts such as Wall Street banking and global design teams, employing methods including interviews, ethnography, and archival research. These studies focus on inflection points of change—before, during, and after transformations—highlighting how new actors and interactions contribute to making sense of shifting realities. Together, the symposium integrates theoretical and methodological diversity, advancing relational approaches to the study of work. The presentations, guided discussion, and Q&A aim to refine existing insights and surface understudied dynamics that contribute to a more nuanced understanding of contemporary and future work. The Future Now: How Organizations Anticipate Digital Innovation Author: Virginia Leavell; University of Cambridge Relational Perspective on Changing Expert Work: Insights from Technology Design to Technology Use Author: Pauli Pakarinen; Aalto University Speaking to Those Who Know: How Experts Manage Knowledge Overlaps with Clients Author: Abhishek Gupte; New York University Author: Callen Anthony; New York University Author: Beth Bechky; UC Davis Relational knowledge at the intersection of platform workers and algorithmic system Author: Stella Kyratzi; The University of Edinburgh Author: Corentin Curchod; The University of Edinburgh
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 | 0.020 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.084 |
| Scholarly communication | 0.028 | 0.033 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 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".