Power in Numbers: Fair Trade as a Civil Society Alternative to Neoliberal Free Trade
Bibliographic record
Abstract
Abstract: This paper examines fair trade as a more positive alternative to the free trade framework that currently pervades the global economy, and as a critique of the wider neoliberal economic paradigm, through secondary research and analysis. The existing free trade model is outdated, perpetuates capitalist hegemony and maintains an oppressive consumerist mindset that is incompatible with an ethical and sustainable future. Fair trade is one potential civil society alternative that already has positive and tangible impacts that should be expanded and refined. This article explores neoliberalism and its drawbacks while presenting fair trade as a critique and possible replacement. It is hoped that this paper stimulates discussion on the negative impacts of the hegemonic neoliberal economic system and the merits of fair trade and incites thought into other possible alternatives for a more inclusive, ethical and sustainable future. Resume : Cet article examine le commerce equitable comme un choix plus positif au cadre de libre-echange qui envahit actuellement l’economie mondiale et present une critique du paradigme neoliberal au sens large, a travers les recherches et l’analyse secondaire. Le modele de libre-echange est demode, perpetue l’hegemonie capitaliste et maintient un etat d’esprit de consommateur oppressif incompatible avec un avenir ethique et durable. Le commerce equitable est une alternative potentielle de la societe civile qui a deja des impacts positifs et tangibles qu’il convient d’etendre et d’affiner. Cet article egalement explore le neoliberalisme et ses inconvenients et presente le commerce equitable comme une critique et une alternative possible. Nous esperons que ce document ouvrira une discussion sur des impacts negatifs du systeme economique neoliberal hegemonique et des avantages du commerce equitable et incitera d’autres solutions pour un avenir plus inclusif, ethique et durable.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.016 | 0.019 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 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".