Role of Knowledge Management in Enhancing the Effectiveness of the Gig Economy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This chapter intricately explores the ever-changing terrain of the gig economy, delving into the intricate facets of knowledge management and its profound impact on bolstering the efficacy of business models. The chapter systematically scrutinizes the nuanced advantages and disadvantages of knowledge management within the gig economy, elucidating its potential to refine operational efficiency, stimulate innovative practices, and elevate the overall customer experience.The chapter culminates with a comprehensive synthesis of its findings and proposes avenues for future research, encompassing cross-cultural studies, longitudinal analyses,experimental methodologies, and the investigation of hybrid business models.Serving as an indispensable resource, this book chapter caters to the needs of researchers, corporate leaders,and policymakers, providing a holistic comprehension of the pivotal role played by knowledge management in navigating challenges and optimizing the myriad opportunities presented by the ever-evolving gig economy landscape.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.001 |
| 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 it