Fluid Workers and Fluid Work Arrangements in the Age of Digital Technologies
Bibliographic record
Abstract
The symposium examines the rise of the “fluid workforce,” which includes gig/app/platform workers, freelancers/independent contractors, paid-crowdsourced workers, moonlighters, or hybrid/remote workers whose roles transcend traditional employment boundaries. Driven by digital technologies and accelerated by the Covid-19 pandemic, fluid work offers increased flexibility and autonomy but also raises critical concerns around job security, social protections, and workplace equity. Despite significant media attention, academic research on the implications of fluid work remains relatively scarce. This symposium, linked to a relevant Personnel Review special issue sponsored by the ILO, seeks to bridge this gap by exploring the organizational, managerial, and well-being dimensions of fluid work arrangements. The five presentations featured in the symposium span diverse worker groups, contexts, and research methodologies, offering valuable insights for future research, and for building equitable and sustainable workforce ecosystems. The Last Stop in Fluid Working: Digital Nomadism in Antalya Author: Murat Atalay; Akdeniz University Author: Umut Dagistan; Akdeniz University Understanding Fluid Workers' Adaptation: A Self-Determination Theory Perspective Author: Ana Junça Silva; ISCTE - University Institute of Lisbon Fully Remote Work Realities: Understanding Employee Experience through Online Employee Reviews Author: Stefano Di Lauro; Mercatorum University Author: Aizhan Tursunbayeva; University of Naples
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".