Bibliographic record
Abstract
Our project closely analyses the No Poverty sustainable development goal (SDG) outlined by the United Nations and delves into how and why this goal can/should be realized globally. Referencing an expansive collection of peer-reviewed research and articles, our team has found that eliminating poverty must start small. There are countless ways in which we, as individuals and Canadians, can make changes to impact poverty on a national and international scale. The University of Windsor has already begun funding initiatives to prevent poverty and homelessness in our community. Canadian governments are constantly working on new and innovative ways to achieve this sustainable development goal. We believe that one of the most significant barriers standing in the way of this SDG is a lack of awareness, understanding, and belief in its potential. Our research will simplify the intimidating goal of globally eliminating poverty and make it more digestible for the individual who wants to make a difference but is overwhelmed by the scope of the goal. Change starts with education; we would like to use our project to educate The University of Windsor's staff and students on how they can contribute to achieving this goal!
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 | 0.050 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".