Performing the future of work: Examining discourses of the future of work and the ideal worker through event ethnographies
Bibliographic record
Abstract
• The future of work is characterized by a plurality of competing imaginaries. • Spaces and events stage performances of expertise that shape imaginaries about the future of work. • Imaginaries of the future of work construct discourses about the 'ideal' worker. • Visions of the 'ideal' worker prioritize qualities such as flexibility and continuous skills upgrading over other factors. The future of work is a multifaceted subject, addressed in academic research, policy, and industry or business contexts. It is often depicted by different imaginaries about the potential impact of new technologies on work. In this paper, I argue that the portrayal of the ideal worker in future of work discourses continues to shift the burden of responding to an uncertain future of work onto the individual. I accomplish this by examining the discourse of the future of work and expertise performances through an event ethnography involving attendance at three major industry conferences, as well as through analyses of four Massive Open Online Courses (MOOCs) on the future of work. In the specific and localized character of the conferences and MOOCs, the idealized worker was framed as flexible, autonomous, resilient, emotionally intelligent, and proficient in using modern AI. These framings reinforce a narrow set of ideal worker norms, limiting which types of work and workers are seen as part of the future of work, and often shift the burden of adapting to structural challenges onto the workers themselves. Ultimately, I contend that researchers, policy makers, and industry must critically examine the discursive and spatial imaginaries shaping the future of work to better understand its uneven and unpredictable impacts across society. Overall, this research contributes to understanding the politics of the future of work, as well as the potential and limitations of researching events and online spaces within the context of economic and labour geographies.
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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 | 0.001 | 0.001 |
| 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".