Responsible, Sustainable Production and Consumption through the lens of Inclusion and Diversity.
Bibliographic record
Abstract
In an era characterised by increased environmental concerns and a growing emphasis on sustainability my Major research project (MRP) delves into the realm of Responsible, Sustainable Production and Consumption through the lens of Inclusion and Diversity. My definition of sustainability, for this project, is using services and related products which respond to basic needs and bring a better quality of life while minimising the use of natural resources and toxic materials as well as the emission of waste and pollutants over the life cycle of the service or product so as not to jeopardise the needs of future generations (World Commission on Environment and Development). For this project, diversity and inclusion are essential lenses through which to understand, address, and advance sustainable production and consumption practices. My goal is to provide insightful analysis of the intersectionality between sustainability, diversity, and inclusion, particularly focusing on how these factors influence production and consumption patterns within immigrant communities addressing particularly the immigrants of Toronto and develop approaches for supporting the adoption of environmentally friendly practices addressing the needs and particular circumstances in immigrants of our society. I will be looking to and involving the targeted users/community in the process a developing, identifying and evaluating existing or new strategies that improve responsible production and consumption practices within these immigrant groups.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.036 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".