Exploring the issue of environmental degradation in the era of mass consumerism and digital media as the solution
Bibliographic record
Abstract
As the price of clothing continues to globally decline, demand and consumption rise in tandem. This relationship between mass production and consumption has earned the title “fast fashion” as it values efficiency and inexpensive manufacturing methods and materials. The environmental costs however, of producing, consuming and ultimately discarding so much clothing is unparalleled. Although there is growing pressure on the industry to engage in more socially and environmentally-responsible practices, this pressure does not in any fundamental way contradict the logic of consumer capitalism. The following research project places the responsibility and agency of conscious consumer behaviour back into the hands of individuals in hopes of disrupting the cycle of mass production and consumption. Leveraging the power and influence of social media, this project reflects upon the current role of Instagram as a propagator of consumer culture and reimagines it as a catalyst for mindful purchasing behaviour among the female millennial demographic.
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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".