The Morality of Informality: Exploring binary oppositions in counterfeit markets
Bibliographic record
Abstract
In seeking to explain the persistence of the informal economy – defined as the set of economic activities that are illegal yet legitimate to some large groups – scholars often focus on instrumental economic factors; in doing so, the role of morality is often overlooked. In response, we conduct a qualitative study of Pakistani counterfeit bazaars, to understand how market participants construct moral legitimacy in a way that justifies participation in, and thus contributes to sustaining, the informal economy. We reveal how the terms ‘counterfeit’ (representing the informal economy) and ‘authentic’ (representing the formal economy) function as an oppositional pair, both within the emic perspective of market participants but also within a baseline etic perspective of Western Intellectual Property regimes. Compared with this baseline, we find that market participants engage in three types of semantic transformation (invalidation, reframing and inversion) that shape moral assessments of authentic and counterfeit consumption. Through our study, we first contribute to a better understanding of how legitimacy in the informal economy is constructed. We also contribute to theory on ‘legitimacy as perception’, indicating how moral legitimization can occur through a dynamic of binary opposition between what is deemed to be ‘moral’ and ‘immoral’. Our final contribution is towards understanding how morality around counterfeit consumption is constructed.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".