Changing Models of Work in the Digital Platform Economy
Bibliographic record
Abstract
The proliferation of online labor platforms has significant consequences for the world of work, as workers adopt novel work roles in the gig economy, digital platforms function as new labor market intermediaries, and organizations reconfigure how they operate. This symposium aims to advance our understanding of this relatively novel phenomenon of platform-mediated work and explore implications for management scholarship. We address the ramifications of the digital platform economy for workers and organizations from organizational behavior, organizational theory, and information systems perspectives, thereby drawing on a variety of contexts. For workers, our symposium provides insights about how individuals can thrive in the so-called ‘gig economy’, the influence of platform algorithms on worker behavior, and the effects of managing multiple work roles. For firms, we explore perceptions of online platforms, motivations for adopting them, implications of online platforms for operations, and how capitalist and cooperative platform firms govern and create value. Finally, discussants will integrate our studies, draw conclusions, and offer suggestions for advancing the topic. No Boss, No Company, No Matter: How Workers Are Making It in the New World of Work Presenter: Brianna Barker Caza; U. of Manitoba Presenter: Susan J. Ashford; U. of Michigan Presenter: Erin Marie Reid; McMaster U. When Algorithms Are Your Boss: Algorithmic Management of Platform Work Presenter: Mareike Moehlmann; Warwick Business School Presenter: Lior Zalmanson; New York U. Presenter: Ola Henfridsson; Warwick Business School Presenter: Robert Wayne Gregory; U. of Navarra I’m Kind of a Big Deal . . . In My Other Job: The Effects of Status Inconsistency Across Work Roles Presenter: Hudson Sessions; U. of Oregon Presenter: Jennifer Nahrgang; Arizona State U. Bringing Work Back In: Theorizing Platform Sourcing as an Organizational Model for Knowledge Work Presenter: Greetje Frankje Corporaal; U. of Oxford Presenter: Vili Lehdonvirta; U. of Oxford Different Paths to Ecosystem Strategy: Platform Capitalism vs. Platform Cooperativism Presenter: Jovana Karanovic; KIN Research, VU Amsterdam Presenter: Hans Berends; Vrije U. Amsterdam Presenter: Yuval Engel; U. of Amsterdam
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".