Bibliographic record
Abstract
The global market is incredibly interconnected and interdependent. However, massive inequalities in wealth and poverty exist throughout, with the richest 1% gaining 82% of the wealth produced last year, while the poorest 3.7 billion people saw absolutely no wealth increase (Ratcliff, 2018). Fair Trade attempts to address these global issues by increasing demand for “ethical” goods. Fair Trade has become a global endeavour involving over 70 countries. Allowing 1.66 million farmers with global sales to reach 7.88 Billion Euros and in 2015, 138 million Euros were paid in social premiums (an additional payment to producers to use for social goals) (Fairtrade. 2018). Despite this, the FLO admits its payments do not meet a living wage (Fairtrade Canada, 2018). This begs the question: is it possible to create a fairer and more just global system through fair trade certification? Or, is Fair Trade fair? Firstly, what is justice or fairness? Justice is born from relations of communities, concerned with giving people what they deserve (Amartya Sen 2009), based on a platform of fairness. Fairness is a necessary component of justice, it’s an equitable structure which justice can be measured (Rawls, 1990; Isbister, 2001). In moral obligation groups, there are principals in attaining justice, equality, freedom and efficiency (Isbister, 2001). Justice is not merely contractual (Wein, 1987), but requires a structure of fairness to measure justice. Fairness requires cooperation and trust. The market derives from social life (Polanyi, 1944, Wood, 1999). Today’s global market is defined by economic globalization and neoliberalism, which perceives the market as an amoral ideological mechanism. Although people are not amoral, the market being from social life, has normative repercussions. Fair Trade exists within market forces, which attempts to address global inequalities. However, fair Trade does not adequately provide a structure of fairness, therefore cannot provide justice.
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.
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 | 0.022 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.033 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.032 | 0.006 |
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".