Exploring Online and Offline Informal Work: Findings from the Enterprising and Informal Work Activities (EIWA) Survey
Bibliographic record
Abstract
The growing prevalence of alternative work arrangements has accelerated with the rapidly evolving digital platform transformations in local and global markets (Kenny and Zysman, 2015 and 2016). Although traditional (offline) informal paid work has always been a part of the labor sector (BLS-Contingent Worker Survey, 2005; GAO, 2015 and Katz and Krueger, 2016), the rise of online enabled paid work activities requires new approaches to measure this growing trend (Farrell and Greig, 2016; Gray et al, 2016; Sundararajan, 2016 and Schor, 2015). In the fourth quarter of 2015, the Federal Reserve Board conducted a nationally representative survey of adults 18 and older to track online and offline income-generating activities as well as their employment status during the six months prior to the surveys. Survey results indicate that 36 percent of respondents undertook informal paid work activities either as a complement to or as a substitute for more traditional and formal work arrangements. We explore the rationale behind respondents' participation in alternative work arrangements by setting questions that capture participant motives and attitudes towards informal offline and online paid work activities. Sixty five percent of qualified survey respondents indicate that a main reason for participating in informal work is to earn extra income.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".