Study authors don’t consider waning SARS-CoV-2 immunity after vaccination in their model
Bibliographic record
Abstract
For decades, the British colonial establishment in the Gold Coast believed that setting its gaze on goldsmiths was pivotal to eliminating pilfery of gold from the mines. This assumption, commonly without concrete proof, hardened colonial paranoia and was shared with Ashanti Goldfields Corporation. Both entities thought that the continuous access to gold by goldsmiths, coupled with increasing gold theft were enough basis to surveil goldsmiths—the supposed pivotal actors in a fledging illicit trade in stolen mine gold. Yet, the problem remained. As this study shows, there was a paucity of successful prosecutions against persons caught in possession of stolen mine gold, and none against a goldsmith. Ultimately, it is argued that from 1907 to 1948, central colonial laws meant to regulate the growing gold mining industry and protect its finds in the Gold Coast reveal negotiations that more than realizing their primary principle(s), increasingly limited access to gold by many indigenes. While the latter sustained an emergent illicit market for pilfered gold from the mines, it simultaneously sparked a misplaced colonial state-led surveillance that targeted goldsmiths.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 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".