The Gig Work Phenomenon: Insights Into Current Multidisciplinary Research and Trending Topics
Bibliographic record
Abstract
This symposium consists of four presentations. Presentation 1-3 provide insights into recent research findings on the topic of gig work relevant in the context of careers, organizational behavior, and human resources management. Presentation 4 – a systematic literature review – provides an overview on existing studies on gig work, and an agenda for future research. Contributions investigate the gig work phenomenon in different countries using a variety of methodologies such as literature review, qualitative interviews, text analysis, participant observation approach, and survey method. The symposium ends with an interactive discussion designed to facilitate proactive exchanges between participants and audience members, as well as networking. A Paranoid Reading of the Gig Economy Presenter: Ana Alacovska; Copenhagen Business School, Denmark Presenter: Eliane Bucher; BI Norwegian Business School Presenter: Christian Fieseler; BI Norwegian Business School The Autonomy Paradox in Platform Work: A Sociomaterial Perspective on The Work of Content Creators Presenter: Farnaz Ghaedipour; McMaster U. Why are Platform Workers Willing to Share Reputational Data? Presenter: Jeroen Meijerink; U. of Twente The Gig Work Phenomenon: A Multi-Level Framework Literature Review and Research Agenda Presenter: Annabelle Hofer; U. of Bern, Work and Organisational Psychology Presenter: Daniel Spurk; U. of Bern Presenter: Caroline Straub; Bern U. of Applied Sciences Presenter: Clara Zwettler; U. of Bern, Work and Organisational Psychology
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".