Conceptualising Consumption Injustices
Bibliographic record
Abstract
Roundtable – Consumption has been depicted as a source of injustice. The term ‘consumption injustice’ is, however, hardly mentioned explicitly in consumer research. We propose a discussion around this topic to nurture further conceptualisation and, thus, encourage research that enables us to explore, envision, and write different (im)possible presents and futures. Invited panelists Jack Coffin, University of Manchester, UK Samantha Cross, Iowa State University, USA Bernardo Figueiredo, RMIT University, Australia Guliz Ger, Bilkent University, Türkiye Wendy Hein, Birkbeck, University of London, UK Daniela Pirani, University of Liverpool, UK Pilar Rojas Gaviria, University of Birmingham, UK Ela Veresiu, York University, Canada
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".