Tracking impacts of poverty and climate change on the farmer's body
Bibliographic record
Abstract
My great-grandfather was from Punjab, a state in India that is well-known for its fertile soil and agricultural prosperity. He was a farmer by profession, and he farmed until the age of 80. Although it may seem like a simple job, he engaged in a lot of physical labour and endured many physical injuries, with the most significant one being breaking his arm in his 70s. Farming also did not offer much profitability or income, so my family was financially unstable, and my great-grandfather was never able to seek immediate medical attention for his injuries. After his retirement, he dealt with the repercussions of his physical labour, including his unhealed arm, but he was fortunate that he would be the last to endure these hardships that came with being a farmer. After generations of my family farming in Punjab, my great-grandfather decided that farming was no longer a viable career option to support a family, so he invested his earnings to fund his children’s education rather than his farming practice. While my great-grandfather worked the fields, my grandfather and his siblings gained sufficient education to work in the big cities of India and find better financial opportunities. My grandfather was able to find a government job to support his family, and my dad eventually immigrated to Canada. As a first-generation immigrant, I find myself in Canada holding the privileges my family previously never had in Punjab, but I have always questioned the sudden disappearance of my family’s roots in farming.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".