Bibliographic record
Abstract
The academic literature commonly exposes large components of informal economies housed in developed countries as nefarious systems designed to help people evade taxes or carry on other illegal activities. However, our community-based participatory action study uncovered a significant element of a social and economic system that was largely undocumented, but was viewed as far more righteous than dishonorable and immoral. Our research involved approximately 375 participants from seven communities spread across a large and sparsely populated geographic region in the northern part of the Canadian province of Saskatchewan. The purpose for the research was to explore how entrepreneurship contributes to the good life , well-being, and prosperity by building social and economic capacity across a rural business ecosystem. We found that an important, yet undocumented part of the business ecosystem was grounded in history, culture, and tradition. When considered through a legitimacy theory lens, this perspective challenges the implication drawn from some of the academic literature that those who participate in informal business systems in developed countries usually do so for immoral reasons that might warrant a legal penalty. Further, we propose that researchers, policy makers, and community development professionals use the term undocumented economy rather than expressions like informal, hidden and underground economy to distinguish components of economic systems based on righteous motivations and activities from those founded on iniquitous practices and non-existent or unofficial record keeping. We begin this article with a definition for the undocumented economy in which we describe why we consider it to be righteous.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.053 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".