"A Market where we all Fit": Adult education and the fair trade market
Bibliographic record
Abstract
The global market continues to create the “great and permanent evils” so well outlined by Karl Polanyi in his book, The Great Transformation. His description of the destructive effects of the emerging self-regulating market during the Industrial Revolution is reflected today in the ongoing erosion of social, environmental, and economic sustainability in both the North and the South. In this downward spiral of life parameters, fair trade opens up the possibility of a market where we all fit, not where we all ultimately fail. Adult education has a vital role to play in opening up spaces where fair trade can prosper and grow, thus contributing to a second great transformation to a more sustainable society. Résumé Le marché global continue de créer des ‘énormes éléments de destruction permanents’ si bien décrits par Karl Polanyi dans son livre La grande transformation. La description qu’il nous présente des effets destructifs du marché autorégulateur durant la Révolution industrielle se traduit aujourd’hui par une érosion continuelle de la durabilité des tissus social, environnemental et économique autant dans l’hémisphère nord que dans l’hémisphère sud. C’est dans cette tendance à la baisse des paramètres de la vie que le marché équitable s’ouvre sur des possibilités d’un marché où chacun a sa place et non où chacun, ultimement, échoue. L’éducation continue des adultes a un rôle vital à jouer dans l’ouverture de créneaux compatibles à l’épanouissement et à la prospérité du marché équitable et, par voie de conséquence, contribuera à l’émergence d’une deuxième grande transformation envers une société plus 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".