Bibliographic record
Abstract
We used to have fresh air."explores the toxic consequences of the ubiquitous use of generators in Lagos, Nigeria's commercial capital and most populous city.Generators are used to supplement the country's epileptic power supply and their use is widely associated with persistent noise pollution.However, the graver concern lies with their emission of carbon monoxide fumes and smoke into the atmosphere.Narrated through the lenses of citizens, this fictional piece highlights the threat posed by these emissions to the health of individuals, as well as to the availability of "fresh air", an immaterial resource prided as the crown jewel of the country's outdoors.The story explores the implications of hazardous communal practices which contribute to the advancement of climate change in a developing African country.We used to have fresh air.Sade was in Lagos to present her team's pitch at their company headquarters.She was advised by Ngozi, her host, to wake up as early as 4am and be at the bus stop before 5am."It is better that way if you want to beat the morning traffic."The headquarters were only an hour away, "but that is when the roads are free.If you get stuck in traffic, you can be there for 4 hours.I kid you not", Ngozi had warned.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.130 | 0.031 |
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".